{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UIp998OHnZSN"
      },
      "source": [
        "##### Copyright 2018 The TensorFlow Probability Authors.\n",
        "\n",
        "Licensed under the Apache License, Version 2.0 (the \"License\");"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "li5wNGR6naj0"
      },
      "outputs": [],
      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\"); { display-mode: \"form\" }\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ykgJW69K4TNL"
      },
      "source": [
        "# Linear Mixed-Effect Regression in {TF Probability, R, Stan}\n",
        "\n",
        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://www.tensorflow.org/probability/examples/HLM_TFP_R_Stan\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/probability/blob/main/tensorflow_probability/examples/jupyter_notebooks/HLM_TFP_R_Stan.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://github.com/tensorflow/probability/blob/main/tensorflow_probability/examples/jupyter_notebooks/HLM_TFP_R_Stan.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a href=\"https://storage.googleapis.com/tensorflow_docs/probability/tensorflow_probability/examples/jupyter_notebooks/HLM_TFP_R_Stan.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "epdzLgKQRfAI"
      },
      "source": [
        "## 1  Introduction"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bMstt69hR44D"
      },
      "source": [
        "In this colab we will fit a linear mixed-effect regression model to a popular, toy dataset.  We will make this fit thrice, using R's `lme4`, Stan's mixed-effects package, and TensorFlow Probability (TFP) primitives.  We conclude by showing all three give roughly the same fitted parameters and posterior distributions. \n",
        "\n",
        "Our main conclusion is that TFP has the general pieces necessary to fit HLM-like models and that it produces results which are consistent with other software packages, i.e.., `lme4`, `rstanarm`.  This colab is not an accurate reflection of the computational efficiency of any of the packages compared."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0axKjgZvRtL9"
      },
      "outputs": [],
      "source": [
        "%matplotlib inline\n",
        "\n",
        "import os\n",
        "from six.moves import urllib\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import warnings\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from IPython.core.pylabtools import figsize\n",
        "figsize(11, 9)\n",
        "\n",
        "import tensorflow.compat.v1 as tf\n",
        "import tensorflow_datasets as tfds\n",
        "import tensorflow_probability as tfp"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IFC9r-h0XlQ3"
      },
      "source": [
        "## 2  Hierarchical Linear Model\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wqS-HhvhDlno"
      },
      "source": [
        "For our comparison between R, Stan, and TFP, we will fit a [Hierarchical Linear Model](https://en.wikipedia.org/wiki/Multilevel_model) (HLM) to the [Radon dataset](http://www.stat.columbia.edu/~gelman/arm/examples/radon/) made popular in [_Bayesian Data Analysis_ by Gelman, et. al.](http://www.stat.columbia.edu/~gelman/book/) (page 559, second ed; page 250, third ed.).\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fAD8am2a4TaY"
      },
      "source": [
        "We assume the following generative model:\n",
        "\n",
        "$$\\begin{align*}\n",
        "\\text{for } & c=1\\ldots \\text{NumCounties}:\\\\\n",
        "& \\beta_c \\sim \\text{Normal}\\left(\\text{loc}=0, \\text{scale}=\\sigma_C  \\right) \\\\\n",
        "\\text{for } & i=1\\ldots \\text{NumSamples}:\\\\\n",
        "&\\eta_i = \\underbrace{\\omega_0 + \\omega_1 \\text{Floor}_i}_\\text{fixed effects} + \\underbrace{\\beta_{ \\text{County}_i} \\log( \\text{UraniumPPM}_{\\text{County}_i}))}_\\text{random effects} \\\\\n",
        "&\\log(\\text{Radon}_i) \\sim \\text{Normal}(\\text{loc}=\\eta_i , \\text{scale}=\\sigma_N)\n",
        "\\end{align*}$$\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5styKLl_MyWu"
      },
      "source": [
        "In R's `lme4` \"tilde notation\", this model is equivalent to:\n",
        "> `log_radon ~ 1 + floor +  (0 + log_uranium_ppm | county)`\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SpurhP2gP_T8"
      },
      "source": [
        "We will find MLE for $\\omega, \\sigma_C, \\sigma_N$ using the posterior distribution (conditioned on evidence) of $\\{\\beta_c\\}_{c=1}^\\text{NumCounties}$."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nj6adNwgPTUP"
      },
      "source": [
        "For essentially the same model but _with_ a random intercept, see _[Appendix A](#scrollTo=tsXhZ4rtNUXL)_.\n",
        "\n",
        "For a more general specification of HLMs, see _[Appendix B](#scrollTo=H0w7ofFvNsxi)_."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LR0ZC0dE4MWb"
      },
      "source": [
        "## 3  Data Munging"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OdboSs9G3JlE"
      },
      "source": [
        "In this section we obtain the [`radon` dataset](http://www.stat.columbia.edu/~gelman/arm/examples/radon/) and do some minimal preprocessing to make it comply with our assumed model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4LjOBqLDV0IQ"
      },
      "outputs": [],
      "source": [
        "def load_and_preprocess_radon_dataset(state='MN'):\n",
        "  \"\"\"Preprocess Radon dataset as done in \"Bayesian Data Analysis\" book.\n",
        "  \n",
        "  We filter to Minnesota data (919 examples) and preprocess to obtain the\n",
        "  following features:\n",
        "  - `log_uranium_ppm`: Log of soil uranium measurements.\n",
        "  - `county`: Name of county in which the measurement was taken.\n",
        "  - `floor`: Floor of house (0 for basement, 1 for first floor) on which the\n",
        "    measurement was taken.\n",
        "\n",
        "  The target variable is `log_radon`, the log of the Radon measurement in the\n",
        "  house.\n",
        "  \"\"\"\n",
        "  ds = tfds.load('radon', split='train')\n",
        "  radon_data = tfds.as_dataframe(ds)\n",
        "  radon_data.rename(lambda s: s[9:] if s.startswith('feat') else s, axis=1, inplace=True)\n",
        "  df = radon_data[radon_data.state==state.encode()].copy()\n",
        "\n",
        "  # For any missing or invalid activity readings, we'll use a value of `0.1`.\n",
        "  df['radon'] = df.activity.apply(lambda x: x if x > 0. else 0.1)\n",
        "  # Make county names look nice. \n",
        "  df['county'] = df.county.apply(lambda s: s.decode()).str.strip().str.title()\n",
        "  # Remap categories to start from 0 and end at max(category).\n",
        "  county_name = sorted(df.county.unique())\n",
        "  df['county'] = df.county.astype(\n",
        "      pd.api.types.CategoricalDtype(categories=county_name)).cat.codes\n",
        "  county_name = list(map(str.strip, county_name))\n",
        "\n",
        "  df['log_radon'] = df['radon'].apply(np.log)\n",
        "  df['log_uranium_ppm'] = df['Uppm'].apply(np.log)\n",
        "  df = df[['idnum', 'log_radon', 'floor', 'county', 'log_uranium_ppm']]\n",
        "\n",
        "  return df, county_name"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hJE3-eC0I-Lm"
      },
      "outputs": [],
      "source": [
        "radon, county_name = load_and_preprocess_radon_dataset()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nV-IAEW2FIqX"
      },
      "outputs": [],
      "source": [
        "# We'll use the following directory to store our preprocessed dataset.\n",
        "CACHE_DIR = os.path.join(os.sep, 'tmp', 'radon')\n",
        "\n",
        "# Save processed data. (So we can later read it in R.)\n",
        "if not tf.gfile.Exists(CACHE_DIR):\n",
        "  tf.gfile.MakeDirs(CACHE_DIR)\n",
        "with tf.gfile.Open(os.path.join(CACHE_DIR, 'radon.csv'), 'w') as f:\n",
        "  radon.to_csv(f, index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ubvf1vHenyCx"
      },
      "source": [
        "### 3.1  Know Thy Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r39MiQV2zUz0"
      },
      "source": [
        "In this section we explore the `radon` dataset to get a better sense of why the proposed model might be reasonable."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GRCyjhSknu9z"
      },
      "outputs": [
        {
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>log_radon</th>\n",
              "      <th>floor</th>\n",
              "      <th>county</th>\n",
              "      <th>log_uranium_ppm</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.788457</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.689048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.788457</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.689048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.064711</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.689048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.689048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.131402</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-0.847313</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   log_radon  floor  county  log_uranium_ppm\n",
              "0   0.788457      1       0        -0.689048\n",
              "1   0.788457      0       0        -0.689048\n",
              "2   1.064711      0       0        -0.689048\n",
              "3   0.000000      0       0        -0.689048\n",
              "4   1.131402      0       1        -0.847313"
            ]
          },
          "execution_count": 0,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "radon.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gdASxsWHjvw4"
      },
      "outputs": [
        {
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bNw8rVqzAuHHjUF1drajr3nvvxeuvv47XX38d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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fbc96630d10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots(figsize=(22, 5));\n",
        "county_freq = radon['county'].value_counts()\n",
        "county_freq.plot(kind='bar', color='#436bad');\n",
        "plt.xlabel('County index')\n",
        "plt.ylabel('Number of radon readings')\n",
        "plt.title('Number of radon readings per county', fontsize=16)\n",
        "county_freq = np.array(zip(county_freq.index, county_freq.values))  # We'll use this later."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "k9yN_rGLlqHE"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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O1kO82bk2gYFeUCpvfYLKrSovv70vPc4sKMiHOzmIgO95WwxiJBnXiqthtlhve6C+jTrA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\ngg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fbcf6b92b10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots(ncols=2, figsize=[10, 4]);\n",
        "\n",
        "radon['log_radon'].plot(kind='density', ax=ax[0]);\n",
        "ax[0].set_xlabel('log(radon)')\n",
        "\n",
        "radon['floor'].value_counts().plot(kind='bar', ax=ax[1]);\n",
        "ax[1].set_xlabel('Floor');\n",
        "ax[1].set_ylabel('Count');\n",
        "\n",
        "fig.subplots_adjust(wspace=0.25)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vuy-t8oNoDTH"
      },
      "source": [
        "Conclusions:\n",
        "- There's a long tail of 85 counties. (A common occurrence in GLMMs.)\n",
        "- Indeed $\\log(\\text{Radon})$ is unconstrained. (So linear regression might make sense.)\n",
        "- Readings are most made on the $0$-th floor; no reading was made above floor $1$. (So our fixed effects will only have two weights.)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zSCWOamv3nXU"
      },
      "source": [
        "## 4  HLM In R"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZPtvJXUdn6an"
      },
      "source": [
        "In this section we use R's [`lme4`](https://cran.r-project.org/web/packages/lme4/index.html) package to fit probabilistic model described above."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3iX5L0srRGIQ"
      },
      "source": [
        "**NOTE: To execute this section, you must switch to an `R` colab runtime.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZBqZjyHdsPIB"
      },
      "outputs": [],
      "source": [
        "suppressMessages({\n",
        "  library('bayesplot')\n",
        "  library('data.table')\n",
        "  library('dplyr')\n",
        "  library('gfile')\n",
        "  library('ggplot2')\n",
        "  library('lattice')\n",
        "  library('lme4')\n",
        "  library('plyr')\n",
        "  library('rstanarm')\n",
        "  library('tidyverse')\n",
        "  RequireInitGoogle()\n",
        "})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Lq3_yATCshI-"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Parsed with column specification:\n",
            "cols(\n",
            "  log_radon = col_double(),\n",
            "  floor = col_integer(),\n",
            "  county = col_integer(),\n",
            "  log_uranium_ppm = col_double()\n",
            ")\n"
          ]
        }
      ],
      "source": [
        "data = read_csv(gfile::GFile('/tmp/radon/radon.csv'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_3gj9hfxshE8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "# A tibble: 6 x 4\n",
              "  log_radon floor county log_uranium_ppm\n",
              "      <dbl> <int>  <int>           <dbl>\n",
              "1     0.788     1      0          -0.689\n",
              "2     0.788     0      0          -0.689\n",
              "3     1.06      0      0          -0.689\n",
              "4     0         0      0          -0.689\n",
              "5     1.13      0      1          -0.847\n",
              "6     0.916     0      1          -0.847"
            ]
          },
          "execution_count": 0,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "head(data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uRqAdn3WsoN-"
      },
      "outputs": [],
      "source": [
        "# https://github.com/stan-dev/example-models/wiki/ARM-Models-Sorted-by-Chapter\n",
        "radon.model <- lmer(log_radon ~ 1 + floor  + (0 + log_uranium_ppm | county), data = data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MuMBVnkAsoMS"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Linear mixed model fit by REML ['lmerMod']\n",
              "Formula: log_radon ~ 1 + floor + (0 + log_uranium_ppm | county)\n",
              "   Data: data\n",
              "\n",
              "REML criterion at convergence: 2166.3\n",
              "\n",
              "Scaled residuals: \n",
              "    Min      1Q  Median      3Q     Max \n",
              "-4.5202 -0.6064  0.0107  0.6334  3.4111 \n",
              "\n",
              "Random effects:\n",
              " Groups   Name            Variance Std.Dev.\n",
              " county   log_uranium_ppm 0.7545   0.8686  \n",
              " Residual                 0.5776   0.7600  \n",
              "Number of obs: 919, groups:  county, 85\n",
              "\n",
              "Fixed effects:\n",
              "            Estimate Std. Error t value\n",
              "(Intercept)  1.47585    0.03899   37.85\n",
              "floor       -0.67974    0.06963   -9.76\n",
              "\n",
              "Correlation of Fixed Effects:\n",
              "      (Intr)\n",
              "floor -0.330"
            ]
          },
          "execution_count": 0,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "summary(radon.model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0qZXx27dp7aZ"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "$county\n"
            ]
          },
          "execution_count": 0,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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E3ZhbZXyABmhZQg0aQNqQmmUMNWgAAEohQQMAUAoJGkCqmKxW3exAfpCgASTJ1H/DvCMHyAxuEkLXMnv27EuXLokdBRdGF5keMllkbPE4EWPhypIlS6TYW5k/SNDQtWzZskXsELjRbmXYIquHgmShiQNAktC7ritADRpAqtjlGHEnc6hBAwBQCjVoAEn6bxt0d7R1yBhq0ADSY36HEN3sZAw1aKDdjz/+uGDBAicLKS0tTUpK4nYGSzGNbtVnY9w4OfSxM6fT6VxdXR1+emJi4ty5czmMRyxI0EC7qKiooiJn+5BpNJrs7GzZTDfaptbs/OtDG9z8NEITB4D0mLc7ow1axlCDBpAkY16uqqoiBDVN2UINGkBimCzcGOwqkKABpARzJHUpaOIA4SxevPiLL74Q5dRlZWXjxo2T+qreJyaVmG8OHTpUr9dL/aIsEua6Vq5cOX78eL7P4gwZvrVArTVr1oh1atn04jCvOJeUlMi1t4Ncr8teaOIAkBJTnw103ugKkKABpAfZuYtAggaQEmMTBzpydBFogwaJuXTp0urVq1mWtetZp06dWrhwoYeHB09RCWNz8CbzzfT09KamJqlflEUdX1dwcPDy5V3iRwQSNEhMr169Zs+ebe+zfvrpp9TUVH9/fz5CEswccmJoYeydzTlzampqfH19RQyJJx1flywv2SIkaJAYDw+P2NjYzo9rzdfXNzo6Wga9OEjhnYexsbFy7e0g1+uyFxI0AL2MDc2YeaPLwk1CABqZ3wbE/cAuCzVokJiWlpbExMSmpia7niW9+aBtm/HZyXmT6cEwzPbt24ODg8UOhC5I0CAxLi4u+/bts/dZkhtJaOOMz2irlTc0cQDQCO3OQFCDBqCEqcqMwdxgggQNdjt16tS5c+fEjsI+165dKygo8PHxETsQy1LOJJseM1kk7958G59YW1vr7e3NT1Bi4va63NzcEhMTuSpNSEjQYLeamprq6mqxo7CPTqfTarUGg0HsQGxi+8tbX1+v1+t5DUYU3F6XQqFgWZZhGK4KFAwSNNjt/vvvv//++8WOwj6FhYVpaWnU3iSc0/qu4Jw5c2x8olxvEsr1uuyFm4QAdEHTM5igBg3ScPv27eLiYnvnSDKprKw8evQotXM4jDs+1vSYySJFcYdsfOKtW7eobVhvj2GYUaNGeXp6ih2IZCBBgzRcv37dWl9gW9y4cePIkSMqlYrDkLjkOdZ869AhWxN0Y2OjhPKdQqHo169fWFiY2IFIBhI0SEPfvn1Xr17t8NPPnDmzdOlSatugV7ftZmfrlaKtVt7QBg0AQCnUoMFxzc3Nubm5kui7Vl5enpOTQ22X4fSKO902mCyyKWizjU+sr6/38vLiJygxOX9dSqXyz3/+s0Kh4CokUSBBg1N8fX0dvnEnJFdXVz8/P3rvp1W02rJ9YQGlUkntXx1nOH9dDMNI4pPZMSRocJybm1tSUpLYUdgkNzdXo9FQ2AZtmvG5dRt0svVntCLXNmi5Xpe9kKABRGM+4zO6P0N7vCfoVatWRUREPP7443yfCMAuhw8ffvnll0UOwmzSZyaLjC22POlzBzicD/rRRx998cUXOSkKuNJRgv7555/79++v0+n27dtXU1MzY8YMuz4Ker3+4YcfLi4u3rlzp9NxAnDswQcffPDBB8WNwXx4N7ucEGJ3R280Bcib1W52y5YtGzJkiMFgWL9+/dKlSzdt2jR37ly7ilYqlUVFRYsWLXI6SAAZwkJW0CmrNei33nrrxx9/VCgUGzdu/OijjwYMGDBgwIBNmzbZVbpCoXBxsfA3gGXZnJyc27dvm/Z8/fXXkZGRDQ0NdpUvIp1OJ4N7xO0Zr0t+E6SxLNvU1MTrB+zIkSNVVVX2PGOG6VHuXe+9956tT4uOjr7rrruMjw0Gg4S+NbaT1nVVV1f//PPP5suwubu7p6WlOT9/ntUEzbKsn5/fiRMnDAbDsGHDGhoampubnTyZCcMw3t7e5uNuVSoVwzAWszmdjC+9hAK2kfFdkN91EUL4vq7a2tqamhrHnmvXEw0Gg+lCpPWtsZ20rothGE9PT/POkQqFgpPZTa0m6KSkpISEhLq6ugULFlRVVWk0mtGjRzt/PpOpU6eabzY2Nur1eg8PDw5PwSuWZVmWlVDANtLpdG5ublJaXNU2DMO4ubnx+n6lpKTYdfxcsyaO5557zrGT1tfXy+9DSKR2XX5+fgEBAcnJtnaOtF1HTRx79uzR6/VJSUmVlZWTJ0/OyMjg/PQAJtXV1fytA9DQ0FBeXl5bW8tT+fa6K7ef+ebFixcdK0er1TpcbReFSqXq1auX2FFIhtUErVAokpKSWJa9efNmUFDQ4sWLHTvBypUrHY0Nupa33377+++/56nws2fPrly50t3dnafy7RaZZ76VmZnpWDHNzc3S+rnj6+u7ZcsWsaOQDKsJ+tdff/3zn//8n//8JzQ09KOPPpo1a1ZOTg7mCQT+/P3vf+evcI1Gs3XrVnpGErbpwpGXl2flwE6gm528WW2G/+tf/zp8+HDjXenw8PAJEybYvgwPAHSgTXbGGEKwxmoN+ujRox988IFSqSSEuLi4LFq0aO3atQIGBpL3+OOP37p1S+wo/lBaWpqUlERLa8DoVgNSxo2zewChCYcjCami0+kmT578wgsviB2IyKwm6MDAwG+++SYuLs64+cMPP6BpH+yye/dusUO4Q6PRZGdnU9LE0aYG7cxKMXJt4pDrddnLaoJ+7bXXJk+ePG7cuOvXr8+cOXPfvn3btm0TMjKArgDtG9ABqwl6zJgxZ86c2bdv34ABA3r27JmVldW7d28hIwPgj8FgSExMNB/LKiin50gycaaJIy0tLTU11eFTgwA6miypR48eM2fOFCwUAMEoFIrCwkKxzu78HEkmaAqQNwsJetiwYdaO/vbbb/kMBqREr9fTM+6jUzqdTqvVGm96U+DOmGAnx+ZotVoJDYm2XafXJdelZNqw8HndunWr8HGA5BQVFb377rtiR2GrU6dOLVy4kJaBKmajVNQb/JNP2zdG3JzkBqrYqNPr8vPz27zZ1pUbpctCgo6OjhY+DpCc8ePHjx8/XuwobEXVQJV2TRwOjlIh8m3ikOt12ctCgvb29j5w4IDFmTd++OEH/kMCEEJZWdl3330n0snvzKqTn5/vTEG1tbWO/dL38fF55JFHnDk1CMBCgv7oo48iIyNzcnLa/MTgcLpRANFdu3bN4SmKOJRyJnm1xxqHn97Q0GA+ba/tunXrhgRNPwsJ2vi2xcfHm9eXb9++fffdd1+5ckW40AD4FB8fHx8fL8qpM9s2cTg4ExlBU4DcWUjQxm6Ver2+Tf9KjUYjUFAA1l2+fNmBxV6NNwlpmGJ4c3CrZYnS09OdKa2pqUnEi5owYUJiYqJYZ+8KLCRonU5HCNFoNAUFBYLHA9AJxya/vXDhwrx589RqNR8h2WVzbqtNhyfyNdJqtX5+fk4F5AQaXk95s9otFNkZ6OTq6tqvX7/Oj2tNpVL17duXkl4c5hy4FnNo4pA3qwn6s88+y8rKMh+JkJCQsGHDBkGiAqnauXPnunXrxI7CgrKysnHjxlExUGVSifnW0KFDnSlMr9dTcVFcM11XZmZmUlKS2OGIxupb++yzzy5fvnz48OGmpQ85WQMR5O3JJ5988sknxY7CAkpms2szjx0hpKSkxNKBtpJrDVqu12Uvqwl64MCBaWlpQoYCslFZWblnzx6xo2ilvLw8JyeHgsHBrVa92BTk7Gi4+vp6Ly8vp4qgia+v7xNPPCF2FBSxmqDj4uKKioqcmUocuiyGYcyXoKeBq6urn5+fj4+PyHFU3HmYd2+++aQcjpHZlBSO9emWMasJev/+/UuXLlWr1Wq12tS4gZGEYAuelqB3Rm5urkajEbeJo037BicvEZoC5M1qgn7zzTeFjANkrLy8fNasWeLGQMWSV9ytdGUi6SWvPD099+zZo1AoxA6EXlYTdGRkZJs9e/bsab8ToFN9+/Z1ZlUnToh4k7D9jUEjTl4T1KDlzWqCPnfu3KuvvlpTU2PcrKuru3r16pQpU4QKDOTs3Xff/f3334U8Y1lZ2RtvvCHS/TQLQ1FWe6xZ4/gMHHc4PBeHAFJSUsLCwsSOQtqsJujU1NSYmJigoKCTJ0/OnDlz27ZtS5YsETIykLGIiAiBV1Dbu3dvdHS0r6+vkCf9w/FWW0VxhwghhMRyUvatW7fEv/NphZzuXorFaoI+c+bMsWPHWlpa4uPj09LSEhISkpOTp06dKmRwIEvNzc0DBgwQ+KR+fn6DBg0KCAgQ+LzqDa36adx8vpqr1PxHgTdvUjjemmEYEQegy4nVBO3j43P+/PmoqKiWlparV6+GhIRcunRJwMBAtvLz8z/55BOBTyrkiir5kXmEkD/WSYlsNRm/k1MjtUftiipvvPFGz549xY5C8qwm6MWLFw8bNuzixYuJiYnjx48PCgqKiIgQMjKQq6eeeuqpp54S+KSCrahiuiWYH5nHLudy8RSLcJNQ3qwm6Pnz5ycnJ6vV6iVLlkRERFRUVGCF9i7ozJkz8+fPFzsKDgjXzc6sLx2TRcYWjzs0uogQMraYl1Ffku5m14Zard61a5fYUdClo2lWgoODjQ9oG3QAgrn33ntF7yHHCWG62bXvUWf26vHyMqIGLW9WE/T999/fZk9YWFhOTg7P8QA46Pfff9++fTvLshb/V6hudq161HHVl64DHXSz69at27PPPos5ziTNaoJeu3at8QHLshUVFW+99dZjjz0mVFQAdnN1de2g161xPmjee6SdabXl5FzPtuhg0ViVSoXsLHVWE/SIESPMNx999NH4+HhMNAXiqqur62CES2ys1R5sfn5+0dHRvPZIuyu3VTq+kHqR2x51FnW8okr7VXFdXV0F7oEOzrB1qu/KysqysjJeQwHo1JEjRxxb68fYxMHv8n2tFxvkvXWDEGL/moSenp7r1q3D9BdSYVMbtMFg+P777zE9NIjusccec6ypTaPRrF27lr+bhG1uD7LLCSGbLB/KKdwklDerCXr16tXmmz4+PlFRUfzHA9C5oqKi//u//7PrKbx3s+Nhpjpb2NLNzsXFZefOnRQOOIROdTRhv5BxANhu3Lhx9mZAXrvZddi7jl+oQcub1QTdp0+fxsbG9vu9vLww5hus2bdvH50rC/Nbg25dfeZpTIpFNAxUGThw4Pr168WNQa6sJugZM2aUlpYuXbo0NDT0ypUrK1euHDVqVEZGhpDBgeRMmjRp0qRJYkdhAX81aEutz8IN7UENWt6sJuj33nvv7Nmzxo79vXr12rVrV2RkZGZmpoCxAY9OnjyZl2dhXojm5maFQiG/u/xnz55duXIlL1Mne7a6WyPwd6SxsdHT01PIMwogODhY+Nla6GQ1QbMse/nyZdMESVeuXGlqahIqKuBdaGioxRH8DQ0Nrq6uov9q5txXX32l0Wj4WMp2TeGdxyUTTxAi6LwINTU14kxyzSc5rVPuJKsJOjMzc8yYMc8880xoaGh5efk777zz3HPPCRkZ8Mq4HHD7/bW1te7u7u3bao8dO3b27FlBQuOFVqstLS3lZwr5O6NRTpw4wUP5Hamvr6c/nfXu3Xv8+PF2PaWqqoqnYKTFaoJ+7rnnYmJidu3a9eOPP/bo0SM7O9velxjkxN3dnY/qp2BcXV39/Px4Gepdceeh8C+RUqmkf+ESfscHyVpHIwlHjhw5cuRIwUIBmg0fPnz48OEdH/Prr7/+9NNPwsRjL2OC5rw1YNzxseabwidohUIh4pJXo0ePRvLlla1DvQE6VV5eLvxvfBvV1NSUlpZy2BqQ2WRhKVjhL1/cRWMHDhyIBM0rJOgupKWlpdM+7PX19W5ubo7dJAwKCqJ26vCioiKNRsPVaLo28yIZXUi9KPAdQtLZZEl8q6+vbz8fk8PCwsIw/V4bSNBdSEVFRacz+Oh0OoVC4eLiIkxIguF4sqTgtvNszLmWLmfh2R4AACAASURBVMjkSG3ZO1kStZRK5ZIlS4KCgsQOhC4WEvSwYcOsHf3tt9/yGQzwKyQkZNOmTmbwsdaLgyvV1dU8ldyxy5cvL1myhKtVvTe3Hix58/lqQlZbOZZfXK3q7ebmRn9vkC7IQoLeunWr8HFAV3Dp0qWXXnpJlFNzvKo3z2t1246rVb2DgoJef/1158sBbllI0NHR0RYP3bNnj7X/ArBFaGioxeGLAuBwVW9LY7vFuSiCod5yZ7UN+ty5c6+++mpNTY1xs66u7urVq1OmTBEqMJCPgoKCN954Q9wYuJwsSaSZRS3idrKk++67b+XKlVyVBs6zmqBTU1NjYmKCgoJOnjw5c+bMbdu2LVmyRMjIQDY0Go1GoxE9Bk4mSxJxZlGLUIOWN6sJ+syZM8eOHWtpaYmPj09LS0tISEhOTp46daqQwQEftm7dumvXLmv/azAYGIaRXy8OTmrQh0a3zcVCzixqEQ3TjTqmb9++uN3VKasJ2sfH5/z581FRUS0tLVevXg0JCcE00PIwa9asWbNmWftfvntxiMX5GnT7ujMRu/pMUIOWO6sJevHixcOGDbt48WJiYuL48eODgoJMM9tBl3X8+PEVK1aIHYUjOKhB01d9JmLUoN9+++27775byDN2ZVYT9Pz585OTk9Vq9ZIlSyIiIioqKlJTU4WMDCgUFxcnep3RMZzXoAWemN8a1KDlraORhCqV6saNG4SQUaNGEUKUSgw7lIAvv/xy0aJFDj/dYDC4uLjIb8RtWVnZuHHjnPoMTyox3xo6dKizMXFBr9fL5oupVCpzc3PDw8PFDoQiVt/a559//s033wwKCjJ9V4cPH757926hAgMHJSQklJSUdH6cFWiDtqh9A7QzLzKHUIOWN6sJurCwsKKiIjAwUMhowGHbt29///33nS8HvTgsE29Z2I5JtxeHNcuWLbv//vvFjoIWVhP08OHDuZr6CwTwl7/85S9/+Yvz5aAGbVGbGjQ9DfGoQcub1QQ9c+bMwYMHJyYmmk8HvnixhTlwgQanTp2aOXOm8+WgDdoyKhugibzaoAkh3t7ehYWFmLbJxOpbu2LFitDQUIZh6urqHCuaZdl58+YZF4LLzc3F33leDRo0iJNWUdSg26O2AZqgBi13VhO0m5tbYWGhtf+1xaFDh6qqqo4dO7Z169Z169b985//dKY0sMWCBQt+/PFHZ0pAG3QbFI4eNCePNuhnnnnm8ccfFzsKGllN0CNHjvzmm29GjBjhcNHFxcXG/nmjRo3asWOHw+WA7V599VUnS0AN2hydowfNoQYtb1YTdHFx8erVq0NCQszXDD579qztRVdXV8fGxhJCQkNDb968af5fLMumpKRotVrTnoqKirS0tDaH0cxgMBBCmpqaxA7Eqr/97W/nzp2z91ksy8qvAZoQcvr06cmTJ9td2Uw43GbHA18++OCXXAXFAQm9X/369Vu7dq2NB+t0Ogllg6qqqsLCwtzcXNMePz+/vLw8598aqwna+XlM1Gr1lStXCCGXLl1q0yGEYZht27bp9XrTnry8PKVSKeLqavZqampiWdbT01PsQKxau3Ztc3Ozvc9yZk1Cms2YMeP111+3t7IZsPHO4xtzqwghZC5dQwGqq6uFX0rcMSqVyvYFE27evCmhbODv7z9t2rRp06aZ9nh4eHDyh9Nygq6oqJg8eXJJSYkzt1Pj4uKMPXNLSkri4uLa/K95xZwQ4unpqdfrJdT0aXz1RQzYYDCkpaXpdDpuizW+CxJ6I2x06tSpRYsW2bWiSn7rZVMyMjK4DooDXK2oIrqJEyempaWZNqV1I8TFxcXLy4uPv5SWE3RQUNDgwYM3btz40ksvOfx3YOzYsXv37p00aZKxvuxEkGCBQqF46623WlpauC22rq7Ozc1NHt95c9OnT1+7dq1daxJuItXqDXe+cp0u5ygKrtYkFB3NP0ZFZLWJo6KiYs+ePWvXrg0MDDT9Kfvhhx9sL5phmDfffNPZAME6X1/fDv63pqYmPT3d3gwu4xq0vWsS5lOz8GAHpFKDfuGFF4xdBsAuVhM0cqvU+fr6vv322/Y+CzVok/zWS3ejBu2MNk2aYCOrCToyMpIQwrLszZs3/f395Vel6grMG8UKCgr279/f6VN0Op1CoZDf23327NlVq1Z5eHjY8ZzgVhk5MzOT45i40NTUZN9FCWXKlCmPPvqo2FFIntUEXVlZOX/+/I8//phhGJZlNRrNxo0bMXeSdMXHx0dFRXV6mFx7cVy4cGHevHm2Vzbvyu3XZg+d8xxotVo6ezvY9WMFrLGaoNPT03v27HnlypUePXpUVVUtW7bsmWee+fjjj4UMDjjk5+dn/k0+f/68xU6pcq1Bl5WVvfHGG3ZUNltXn+dcS1+zhvuonCd6DdrNzW39+vXy+4tOCasJ+ujRo9euXTO2RXbv3v21114LCQkRMDDgV58+fSxWCVGDJtKpPhMKatAMw8jv00IPqwna39//hx9+MA4FJIScOXNGKv3hwRbu7u79+rVNQ4T/od67d+/+9ttveSq8A+Xl5Tk5OSqVyqajPVebby1uzNy8mZeonNfY2Ojp6fnQQw9RND8IcMdqgl65cuW4ceOmTp3ap0+fX3/9dffu3W+99ZaQkYEsDRs2LDQ0VPjzfvXVVxqNxsZKxprWs4QlJyfzEhMXampqfH19g4ODxQ4EeGE1QU+bNi0mJmbfvn0VFRX9+/f/6quv+vfvL2RkIA8FBQV2TeHCk8rKyqNHj9o8MjbW9Gi1x5pDh3gKigMNDQ1tfhYolcp58+bJr6Nk19TRVN/9+/d/8cUXBQsFZKlfv362Nizwae/evdHR0R0P7bnj+J2HplY+Ot26dct8SQ1CCMMwcprCv4uz+kZ+9tlnWVlZtbW1pj0JCQkbNmywdjzQ6csvvzx//rztxzc1NSmVSvl9wxsaGi5duuTAcImLFy/yEQ9X6uvr2/8suHDhgijBGHl6eqampooYgJxY/R4+++yzy5cvHz58uGkuDqnMagjm7J3DpbGx0dXVVX4J2tXV1c/Pr01l06KUM61anCm/N65UKmkbpGfXeHromNXv4cCBA80nlwJ6NDU1FRYWsiwrdiAAFty+fTs/P7/Tw8LCwuhZ2pFaVhN0XFxcUVER+u5QqLm5mb+5zOXaxKHT6bRarXGZBWvSK+a031ldXc1bUByor683n1ddQij/aUIJq9/D/fv3L126VK1Wq9VqU+OGXbPZAU98fHzmzLGQSpx09uzZq1evNjQ0uLm5yS9Bq1Sq0NDQTm4SVrTdURR3iBALvcXp0f4mISdcXFweeOABhULBeclgF8xmB384e/bs+fPnb9++rVQq5ffNrKmpKS0t7aCbXWZT27GCqz3WnDjBc1hOa9/NjisjRoxwZr0O4EQns9mBbFy7dq3jFRQHDx48ePBguQ71Lioq0mg0HQz1zsxttXkh9SIh9I5PMeFvqPdvv/1mcb+Li4soQ426Jrn9kgVr1q1bV1dX1+lhXWqypM3/nRFpzrV089mRqJ0aqT1RJkt65ZVX+GhXgfaQoLuKdevW2XIY33NxiEWj0axdu7ZHjx7mOzdn/fdB67nr6Jyb36Kqqip7V8IFCUGCplFhYeHrr78uyqkNBoO01uu0UWlpaVJSkvkfnkOji8wPGFs8zrhnbLGU+i7pdDoJtUe9+eabmDHCLhYSdJtahhHDMEOGDPnss8/4DwnIxIkTJ06cKMqpZVyDzs7ONn22may2BxQVmfJ1Udv/oxhq0PJmIUGXlZURQt5+++2jR48uX748NDT08uXLq1atGj9+vODhAY2ys7M//PBDsaOwj3kNuk3dmUit1mxOp9MtXLhQrD/nwDfG2oC00NDQ06dPm/rZNDY2RkZG8jcvQU5Ojl6vnzlzJk/lc66xsZFlWRqmAeKW7GvQ7evO7HIxAuKIXGvQ0rqubdu2KZVKPoZeW22D1ul0V65cueeee4ybV65ckeiAJakoLS19+eWXxY6C6PV6FxcX+bVBnzhxYtasWe7u7iQyz3x/8umUlBSxguJAc3Mz5X9NGYbZsGFDz549xQ5Ekqwm6AULFowZMyY9PT00NLS8vPydd95ZtGiRkJF1NVFRUTR0Hqirq3Nzc6P8O++A6dOnr127tv8H4W320/CaO+PmzZu2r4QrFjqXtZUEqwl64cKFQ4YM2bNnzw8//BAcHPzBBx88+OCDAgbWtfzyyy95eXmdH8c/uY4kLC8vz83NJYp/mO9c7bGG2rWsbMTfSEJOREREaDQasaOQMMsJuqKiYsyYMSUlJQ899JDAAXVNPj4+lEwML9e5OIwT9pMf7+wpijtkvnKKRPE0FwdX0LLhJMvfw6CgoMGDB2/cuPGll17CNNAC6N69+9ixYwkhWq22pKRE7HAAnNK/f/8+ffqIHYUcWK0oVVRU7NmzZ+3atYGBgaZbRpjNjm8VFRUnRJ2hR65NHMbJkojicdMecV9nrtDZxKFQKJCgOYHZ7OgSEREREREhYgBy7WZ3/Pjxla0boBcvbjt9nRRJqzsa2MuO2ez27NmDKe4EcPLkSRFzh4yHerdpcJbq0JTWqBrq3bt3723btokdhaxYTdDnzp179dVXa2pqjJt1dXVXr16dMmWKUIF1XTExMWbDjoUm1xq0RqPZZ7bJLifSGtJtDWrQ8mY1QaempsbExAQFBZ08eXLmzJnbtm1bsmSJkJGBva5fvz5//nwn1yqU8UAV8xo0k0WST0t5gMp/8TFQJT4+fu7cudyWCY6xmqDPnDlz7NixlpaW+Pj4tLS0hISE5OTkqVOnChkc2KVnz57vvPOOk4XIeKDKNbPNm89XEyLtISpGfAxUEX6CabDGaoL28fE5f/58VFRUS0vL1atXQ0JCLl26JGBg0LmSkpJffvmF2zIbGxtdXV3l1w/64H0HzDcPHTokViTcqq2t9fb25q/8u+66KyYmhr/yoWNWv4eLFy8eNmzYxYsXExMTx48fHxQUJG7vAmhPq9Vyvua0XFf1boPytbptx/eq3lqtlr/CoVNWv4fz589PTk5Wq9VLliyJiIioqKhITU0VMjLolHFsC7fkepMw3WwSO3Y5IYT7ZdFFgZuE8tZRRSk4ONj4IDlZAqtnStHbb7/98ccfix1FK3LtZkfM5oCWRwc7I6q62bWRmZmJuSKchBVVxJSRkZGRkSF2FK3IsgbdZg5oEXsxcg41aHnDiiriqK+vX716tU6nEzuQtpqbmxUKhZyGeq/xXG2+ubgxMzNTrFi419jY6OnpKXYUbbm6ui5evLhbt25iByJ5FhK0r68vIeSdd94xrajSs2fPDz/8MDIy8plnnhE6QJny8PBISEhoaWkRO5C25Deb3ZrjrTb5aLgXEZ2z2bm5uaGvHiewooo4FArFmDFjxI7CAhk2cZglaHY5IURWCRpNHPKGFVVEtmTJkm+++UbsKO6Q4U1Cmd4eNKLqJmFKSsrs2bPFjkJWsKKKyFatWiV2CK3IrAYt49uDRqhBy1tHTY0PPfQQesnw55NPPvnpp5/EjqIt2c0HfWdewKWGZWvWeIkYCh+omg/ay8vr2WefldXPL7FZTdCfffZZVlZWbW2taU9CQsKGDRsEiapL6NGjR3Nzs9hRtCW3od5n7jzs27cvhffTnMT3UG+7eHp6YgEmbln9Hj777LPLly8fPny46RXHS8+tUaNGiR2CBXJq4mjTvqHRaCz28Zc0NHHIm9UEPXDgwLS0NCFD6SJef/31wsJCsaOwSjY3CQ+NbtXc3OPNwKR/RcjjD485qm4SMgzz7rvvhoSEiB2IfFhN0HFxcUVFRfK76y26+fPnz58/X+worJJrDfq+++7Lzs5GDRqkxWqC3r9//9KlS9VqtVqtNjVuYNFY592+ffvq1atiR2FVfX29m5sbPZUyh92V2898s6Ghoby83PyeijxotVrTskeiU6vVfn5+YkchK1g0VmhffPHF9u3bxY7CKvmsqBKZZ7519uzZlStXuru7ixUOT/hYUcVhw4cPX7hwodhRyAoWjRXahAkTJkyYIHYUVsmjiaPN7UFCSGxs7NatW9HEAdKCRWOhc3q9/pNPPqFw5hCLUs60nR037978NfuuFRQUUN7N7p577hk4cKDYUQBFsGgsdK6pqamqqkrsKBy0KWhzdTXR6XRardZgMIgdTkdu3rwpdghAFywaC53r1q3bnDmSWYJkTusmDmPkhYWFaWlp8mviAHnDorHScOHChY8++kiAE8lgqHdm02LzzTVr1hBCysrK3njjDeP0uXLC4VBvjUaDdUdpg0VjpcHDw6Nfv36dH+c0OQz1PtNqy/i6qVQqDPXumPxeHBmw+j3MyMhos2jsE088IWRkYC4kJESYlSGl24ujfc8N8scE0MmEkNzcXAz1BsmxmqBjY2NNw1KSk5Nv37599913X7lyRajAuop///vfx44dEzuKO6TcxHGnZWO1xxrjgzV//IsmDkfcfffdjz/+OE+Fgy0sJGjjKDK9Xt9mOJlGoxEoqK7E19dXmLYLG0m4icOsZaP9S4omDgcEBwfzVDLYyML30LiSqUajKSgoEDyeLicyMpKq4T8SbeJo077RvjkITRwgRVYrSu+//77xwfnz57/66quRI0eGh4cLFRV05Ouvv+avrUmKNeg2I1Py7s3Pz297zLVrEhio4oBOa9BqtRrLbkiXhe/hxYsXp0yZ0qdPn3379l2+fDkmJmbIkCEvvvjihx9+6MA7vWrVqoiICLRkcej69evV1dU8Fd7U1KRUKqWVoNuw+OJIYqCKA+rr6ztezbmpqUmwYIBzFr6Hf/3rX5OSkv73f/+XELJixYp58+b985//NC6wYleC1uv1Dz/8cHFx8c6dOzmLFwiZPHkyf4VLq4mjfc8NdjkhxMKYGrkOVEETh7xZSND//ve/8/LyXFxcWJbdv3+/cXb5MWPGPPnkk/YVrVQWFRUtW7aMm0i7Bq1Wy7KsiAHU1dW5ublJIkGrN/ibHt98/o9as7WfFsYatKR/GVik1WrtnXrQ29tbfq+DXFl4n1iWNb7lJ0+e1Ol0sbGxhJCmpiYHlrxSKBQWPz0sy06ePLmhocG0p6KiIi0tTUJzERh/LHP7+/HatWuiT3hi/PMgjeXNhuw1PVRv8E/8rqMfFqWlpfPnz5fEHx67tLS02Jugp02b9vDDD/MUD1d0Op2EssGNGzcOHDiQm5tr2qNSqfbu3ev898hCgh42bNj69evnzp37yiuvaDQa49u/Y8cOY6bu1JYtWwoKCmJiYrKyLI0cIIQQwjDMJ598Yr4nJydHr9er1Wo74xdNY2Mjy7LcdkFVq9V79+7t/Dg+SaiJw7x9g11OCOnopdNoNFhRRUKqqqoklA0CAgKefvppPtYItJCgX3311YkTJ2ZmZoaEhBw7duz27dsTJ0787rvvDh48aEuJs2fPnj17NtdxAgB0ORYS9IABA37++WfjBEkMw+h0utmzZz/44IOBgYHCxydjCxcuvHz5sthRtCWVFVXyWy+YkpKS0vHxJ06cmDVrFlZUsehvf/vbkCFDOIkHuGX5XgHDMH/605+Mj11dXTv99Hdg5cqVDj9X3l588cXGxkaxo2hLKmsS5ue22ly9enXHx8+ZM8e4xiaPMYlBq9U6uQygi4uL6csOtMHNXNEEBQWJHYIFUmmDZpffaYNmlxNCOhkubxzqjTZokBbaf8kCWGRx7joAmUENWkzl5eWzZs0SO4pWDAYDwzD0t0GT0UWmh0wWGVs8ruPDS0tLk5KS6P9lYC+dTudwe1RKSgru51MOCVpMffv2LSoq6vw4AUmliaNdH7tOXkZ0swMpor6iBNBOu+wMIE+oQYvAYDB8//33YkdhWUNDg6urK829OIYWthowdeLECVueVVNTU1pa6u/v3/mhklJTU+Pr6+vYc/v16ye/F0RmkKBFcP36dQsTYtKhublZoVBQvaKKZ6sEbeMraZxulL/FR8TS2Njo6enp2HMfeeSRhIQEbuMBbiFBiyAkJKTTfrtiob8NenXrJg4bX8kzZ84sXboUbdAgLWiDBglDAzTIG2rQ/DIYDLdu3RI7CjvQP92o+SyjTNadiUY7hulGbaRSqeQ3IF665PZ5pc0333zz2muviR2FHSQwF0frWTjS09NtedKpU6cWLlwov9TDyVwc5hISEjIyMjgsEJyBBM2vUaNGjRo1Suwo7EB/GzRp3Qadl5dn/cA7NBrN1q1b0QYN0kJxRQmgHSaLMFl3mp7RBg3yhho0SIap4myeowFkDAmaLhs2bCguLhYxAKrboM1an5ksknzajllwu9R80GvWrAkLCxMlHuAWEjRdZs+enZqaKmIANPfiyN/QanPTpk22P3f69Olr164NCAjgOCax3bx5s/0k1w6PLQTaIEHTxdPT0+GBYZxQKpXU3iRsNwe0HcOUXV1d/fz85DeyuaWlRX4XBSZI0EAvYzo2b25G0zN0KVQ2NQK0viUI0DWhBs2v8+fPT58+nWVZsQOxlcFgcHFxYRhG7EAImVRiejh06FAnCysrKxs3bpz8RhLq9XprF+Xl5VVQUID2aEmT2+eVNuHh4d9++63YUdiBnoEq5hXnkpIS6wfaBBP2gxShiQMohdEoAKhBA72QmqGLQ4IGGmFRKwCCBE2VnTt3lpaWihsDLSuqeN6Zhp/JIosbM50s7+zZsytXrpT0iioMw7z00kvo9dylIEFT5L777uvfv7+4MVCyJuGawlabycnJThb41VdfaTQaqWc3Ly8vsUMAQSFBUyQsLEz0KRRo6cVhlqDZ5YSQWKtH2sbX1zc6Olp+vThA3pCggUZodwYg6GYHAEAt1KCBLui/AWCCBC0rJSUlH330kTMliN+Lw6z/Rmams503TETpxTFjxoyoqCghzwgygwQtK2FhYU52eBC9F4d5/w3nO2+YiNKL409/+pOQpwP5QYKWle7duzs5M4P4vTjMEnRsrLOdN0zQiwOkCAka6IJ2ZwAT9OIAAKAUEjSID1PyA1iEBA0iM2ZnJgtpGqAttEFLQ0tLy/z585ubm/k+kU6nUygULi4C/eXeHNxqZe709HSeTnTq1KmFCxd6eHjwVH4HnnjiiTFjxgh/XpABJGhpcHFxefHFF1taWvg+UX19vZubm2Dd7DbnttpcvHgxTye6cOHCvHnz1Go1T+V3AF1HwGFI0JIRGhoqwFkE7mbHLr/TssEuJ4T04+lEKpWqb9++yJUgLUjQIDL0qwOwBjcJAQAohQQNAEApJGgAAEqhDRqE1vquIABYhQTNpUOHDlVXV4sdhVMaGxtdXV2VSr4+GCln7kxQx2SRvHvzeTpRG9euXSsoKPDx8RHmdFxRKBQajYa/twMohzeeS1qtVuoJuqmpSalUCpYRBHu5dDqdVqs1GAzCnI4r7u7uAnR+B2ohQXMpKSlJ7BCcxXc/6Dltmzjm8HSiNgoLC9PS0tAPGqQFCRqEhqZnABuhFwcAAKWQoAEAKIUEDQBAKSRoAABKIUEDAFAKvThAaBhJCGAj1KBBUObrWmGNK4COoQZth++++2727NnGx8bxXYItDSUYg8Hg4uLCMAxfJ5hUYr41dOhQvk7UWllZ2bhx42Q2Zrpnz547duwQOwrgkaw+r3wbMmRISckf+aWxsZFlWZVKJW5InON7JGGbWrPp9eSbRqPJzs6W30jCqqoqsUMAHsmtAgiUM293Rhs0QMdQgwahIS8D2Ag1aAAASvGYoHU6XUpKSnR0dGxsrGBNjQAAssFjgt61a5enp2dpaenGjRtfeOEF/k4EACBLPLZBh4eHx8TEEEJ69+7t6ura5n8PHjxYW1tr2vzPf/7Tv3//pqYm/uLhlk6nY1lWQgHbSK/XMwwjv0niWZZtbm6W3/vV0tIiv4siUruuurq6srIyT09P0x5vb+9HH33U+ZJ5TNAjRowghJSWlmZkZKxatcr8v1iW/emnn27fvm3ac+PGjdDQUL1ez1883DKuzSGhgG3U0tIiuWVHbKTX6+X3frEsK7+LIlK7rubmZq1We/HiRdMeLy8vlmWdH0/AfYLesmVLQUFBTExMVlZWdnZ2fn5+dnZ2RESE+TEMw7Rp9MjJydHr9d26deM8Hp7ItR80y7K89oMWC8MwKpVKQh8wG92+fVt+F0Wkdl1qtXrs2LFpaWmcl8x9gp49e7ZxuN3nn39+9OjRAwcO8DgsDQBAvnhs4jh48ODhw4cHDRpECAkLCysoKODvXAAA8sNjgl6/fv369ev5Kx8AQN4wkhAch4lDAXjVRRN0fX19c3OzMyU0NTWxLGveEUUe6urq3NzcbLlJqN7gb3rMZJGbz1fzGZezdDqdVquleTY7Nzc3Ly8vsaMAutD7eeXVihUrysvLnSnB2BdNoVBwFBEt9Hq9i4uLTdOoRuaZb6Wnp/MVExdOnTq1cOFCd3d3sQOxKjw8/OWXXxY7CqBLF03Qr7zyipMlyLWbne3TjbaZODQvL8/KgVTQaDRbt26V33SjIG9dNEGD84ztzkwWGqAB+ILZ7MApyM4A/EGCBgCgFBI0AAClkKABACiFBA0AQCkkaAAASiFBAwBQCgkaAIBSSNAAAJRCggYAoBQSNAAApZCgAQAohQQNAEApJGgAAEohQQMAUAoJGgCAUkjQAACUQoIGAKAUEjQAAKWQoAEAKIUEDQBAKSRoAABKIUEDAFAKCboLYbIIkyV2EABgMyTorsKUmpGjAaRCKXYA3Pviiy82bdrE91kMBgMhRKFQ8H0izkTmmR4yWST5dIrFo/R6vYuLi4uL3P5ynzhxYtasWe7u7tYOiIqKWrZsmZAhAXRKhgl65MiRYWFhfJ+lqamJZVlPT0++T8SV/Nw7jy+kXiRktcXD6uvr3dzcXF1dBQpLKHPmzFm6dKlarbZ2gJ+fn5DxANhChglapVL169eP77M0NjayLKtSqfg+ER86eH1qa2vd3d3d3NyEjEcAKpWqb9++Tn6FowAAA8pJREFUPXr0EDsQADvIMEGDRexysSMAADvJrakRAEA2kKABACiFBA0AQCkkaAAASiFBAwBQCgkaAIBSSNAAAJRCggYAoBQSNAAApZCgAQAohQQNAEApJGgAAEohQQMAUAoJGgCAUkjQAACUQoIGAKAUEjQAAKWQoAEAKIUEDQBAKSRoAABKUbRo7JEjR/R6vdhR2Eqr1bIs6+/vL3YgHLtx44ZKpZLoauUduHjx4o4dO3x8fMQOhGM3btwICAgQOwruSeu6jh8//tBDD/FRMi0JeuzYsZ6enmJHYYdz5841Nzf369dP7EA4Vlxc3L9//5CQELED4Zi/v7+/v7/8EvSHH36YkZEhdhTck9Z1TZw4cfTo0XyUzLAsy0e5svf+++83NDTMnj1b7EA4tnTp0kceeYSnT5uIkpKSNm3a1L17d7ED4VhCQsKXX34pdhTck+t12Qtt0AAAlEKCBgCgFBI0AAClaLlJKDlubm4S6nNiO1dXVzc3N7Gj4J67u7tSKcNPu5eXl9gh8EKu12Uv3CQEAKAUmjgAACiFBA0AQCkkaAAASiFBAwBQCgnaQTqdLiUlJTo6OjY2tqSkROxwOLZq1ardu3eLHQU3WJadO3fu/fffP2HChKqqKrHD4Zic3ikjeX+z7IUE7aBdu3Z5enqWlpZu3LjxhRdeEDsczuj1+jFjxvzjH/8QOxDOHDp0qKqq6tixY1OnTl23bp3Y4XBGfu+UkVy/WY6RYc9QYYSHh8fExBBCevfu7erqKnY4nFEqlUVFRcuWLRM7EM4UFxePGjWKEDJq1KgdO3aIHQ5n5PdOGcn1m+UYJGgHjRgxghBSWlqakZGxatUqscPhkkKhcHGRz0+r6urq2NhYQkhoaOjNmzfFDodLMnunjGT8zXIAErR9tmzZUlBQEBMTk5WVlZ2dnZ+fn52dHRERIXZczjK/LrFj4Zharb5y5Qoh5NKlS2q1WuxwoHNy+mY5CQnaPrNnzzZOMfr5558fPXr0wIEDDMOIHRQHTNclP3Fxce+//z4hpKSkJC4uTuxwoBMy+2Y5CQnaQQcPHjx8+PCgQYMIIWFhYQUFBWJHBJaNHTt27969kyZNYhhm27ZtYocDncA3yxzm4gAAoJTc7jAAAMgGEjQAAKWQoAEAKIUEDQBAKSRoAABKIUEDAFAKCRoAgFJI0AAAlEKCBgCgFBI0AAClkKABACiFBA0AQCkkaAAASiFBAwBQCgkaAIBSSNAAAJRCggYAoBQSNAAApZCgAQAohQQNAEApJGgAAEohQQMAUAoJGgCAUv8fHurHuodwT/0AAAAASUVORK5CYII="
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "qqmath(ranef(radon.model, condVar=TRUE))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nCsGcLnP40Lg"
      },
      "outputs": [],
      "source": [
        "write.csv(as.data.frame(ranef(radon.model, condVar = TRUE)), '/tmp/radon/lme4_fit.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2XrrSLW43pHL"
      },
      "source": [
        "## 5  HLM In Stan\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-ddXXuiWnv2-"
      },
      "source": [
        "In this section we use [rstanarm](http://mc-stan.org/users/interfaces/rstanarm) to fit a Stan model using the same formula/syntax as the `lme4` model above.\n",
        "\n",
        "Unlike `lme4` and the TF model below, `rstanarm` is a fully Bayesian model, i.e., all parameters are presumed drawn from a Normal distribution with parameters themselves drawn from a distribution."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c6IpkPOOnsmQ"
      },
      "source": [
        "**NOTE: To execute this section, you must switch an `R` colab runtime.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "s-p-rAMZuaGh"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).\n",
            "\n",
            "Chain 1, Iteration:    1 / 2000 [  0%]  (Warmup)\n",
            "Chain 1, Iteration:  200 / 2000 [ 10%]  (Warmup)\n",
            "Chain 1, Iteration:  400 / 2000 [ 20%]  (Warmup)\n",
            "Chain 1, Iteration:  600 / 2000 [ 30%]  (Warmup)\n",
            "Chain 1, Iteration:  800 / 2000 [ 40%]  (Warmup)\n",
            "Chain 1, Iteration: 1000 / 2000 [ 50%]  (Warmup)\n",
            "Chain 1, Iteration: 1001 / 2000 [ 50%]  (Sampling)\n",
            "Chain 1, Iteration: 1200 / 2000 [ 60%]  (Sampling)\n",
            "Chain 1, Iteration: 1400 / 2000 [ 70%]  (Sampling)\n",
            "Chain 1, Iteration: 1600 / 2000 [ 80%]  (Sampling)\n",
            "Chain 1, Iteration: 1800 / 2000 [ 90%]  (Sampling)\n",
            "Chain 1, Iteration: 2000 / 2000 [100%]  (Sampling)\n",
            " Elapsed Time: 7.73495 seconds (Warm-up)\n",
            "               2.98852 seconds (Sampling)\n",
            "               10.7235 seconds (Total)\n",
            "\n",
            "\n",
            "SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).\n",
            "\n",
            "Chain 2, Iteration:    1 / 2000 [  0%]  (Warmup)\n",
            "Chain 2, Iteration:  200 / 2000 [ 10%]  (Warmup)\n",
            "Chain 2, Iteration:  400 / 2000 [ 20%]  (Warmup)\n",
            "Chain 2, Iteration:  600 / 2000 [ 30%]  (Warmup)\n",
            "Chain 2, Iteration:  800 / 2000 [ 40%]  (Warmup)\n",
            "Chain 2, Iteration: 1000 / 2000 [ 50%]  (Warmup)\n",
            "Chain 2, Iteration: 1001 / 2000 [ 50%]  (Sampling)\n",
            "Chain 2, Iteration: 1200 / 2000 [ 60%]  (Sampling)\n",
            "Chain 2, Iteration: 1400 / 2000 [ 70%]  (Sampling)\n",
            "Chain 2, Iteration: 1600 / 2000 [ 80%]  (Sampling)\n",
            "Chain 2, Iteration: 1800 / 2000 [ 90%]  (Sampling)\n",
            "Chain 2, Iteration: 2000 / 2000 [100%]  (Sampling)\n",
            " Elapsed Time: 7.51252 seconds (Warm-up)\n",
            "               3.08653 seconds (Sampling)\n",
            "               10.5991 seconds (Total)\n",
            "\n",
            "\n",
            "SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).\n",
            "\n",
            "Chain 3, Iteration:    1 / 2000 [  0%]  (Warmup)\n",
            "Chain 3, Iteration:  200 / 2000 [ 10%]  (Warmup)\n",
            "Chain 3, Iteration:  400 / 2000 [ 20%]  (Warmup)\n",
            "Chain 3, Iteration:  600 / 2000 [ 30%]  (Warmup)\n",
            "Chain 3, Iteration:  800 / 2000 [ 40%]  (Warmup)\n",
            "Chain 3, Iteration: 1000 / 2000 [ 50%]  (Warmup)\n",
            "Chain 3, Iteration: 1001 / 2000 [ 50%]  (Sampling)\n",
            "Chain 3, Iteration: 1200 / 2000 [ 60%]  (Sampling)\n",
            "Chain 3, Iteration: 1400 / 2000 [ 70%]  (Sampling)\n",
            "Chain 3, Iteration: 1600 / 2000 [ 80%]  (Sampling)\n",
            "Chain 3, Iteration: 1800 / 2000 [ 90%]  (Sampling)\n",
            "Chain 3, Iteration: 2000 / 2000 [100%]  (Sampling)\n",
            " Elapsed Time: 8.14628 seconds (Warm-up)\n",
            "               3.01001 seconds (Sampling)\n",
            "               11.1563 seconds (Total)\n",
            "\n",
            "\n",
            "SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).\n",
            "\n",
            "Chain 4, Iteration:    1 / 2000 [  0%]  (Warmup)\n",
            "Chain 4, Iteration:  200 / 2000 [ 10%]  (Warmup)\n",
            "Chain 4, Iteration:  400 / 2000 [ 20%]  (Warmup)\n",
            "Chain 4, Iteration:  600 / 2000 [ 30%]  (Warmup)\n",
            "Chain 4, Iteration:  800 / 2000 [ 40%]  (Warmup)\n",
            "Chain 4, Iteration: 1000 / 2000 [ 50%]  (Warmup)\n",
            "Chain 4, Iteration: 1001 / 2000 [ 50%]  (Sampling)\n",
            "Chain 4, Iteration: 1200 / 2000 [ 60%]  (Sampling)\n",
            "Chain 4, Iteration: 1400 / 2000 [ 70%]  (Sampling)\n",
            "Chain 4, Iteration: 1600 / 2000 [ 80%]  (Sampling)\n",
            "Chain 4, Iteration: 1800 / 2000 [ 90%]  (Sampling)\n",
            "Chain 4, Iteration: 2000 / 2000 [100%]  (Sampling)\n",
            " Elapsed Time: 7.6801 seconds (Warm-up)\n",
            "               3.23663 seconds (Sampling)\n",
            "               10.9167 seconds (Total)\n",
            "\n"
          ]
        }
      ],
      "source": [
        "fit <- stan_lmer(log_radon ~ 1 + floor  + (0 + log_uranium_ppm | county), data = data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KZNNvBB8TWTW"
      },
      "source": [
        "**Note**: The runtimes are from a single CPU core. (This colab is not intended to be a faithful representation of Stan or TFP runtime.)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yNKzX9rnuz9H"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "stan_lmer(formula = log_radon ~ 1 + floor + (0 + log_uranium_ppm | \n",
              "    county), data = data)\n",
              "\n",
              "Estimates:\n",
              "            Median MAD_SD\n",
              "(Intercept)  1.5    0.0  \n",
              "floor       -0.7    0.1  \n",
              "sigma        0.8    0.0  \n",
              "\n",
              "Error terms:\n",
              " Groups   Name            Std.Dev.\n",
              " county   log_uranium_ppm 0.87    \n",
              " Residual                 0.76    \n",
              "Num. levels: county 85 \n",
              "\n",
              "Sample avg. posterior predictive \n",
              "distribution of y (X = xbar):\n",
              "         Median MAD_SD\n",
              "mean_PPD 1.2    0.0   \n",
              "\n",
              "Observations: 919  Number of unconstrained parameters: 90 "
            ]
          },
          "execution_count": 0,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "fit"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iO6In1K3uz7B"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              ""
            ]
          },
          "execution_count": 0,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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"
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "color_scheme_set(\"red\")\n",
        "ppc_dens_overlay(y = fit$y,\n",
        "                 yrep = posterior_predict(fit, draws = 50))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZAMwe8rWvId4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              ""
            ]
          },
          "execution_count": 0,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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"
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "color_scheme_set(\"brightblue\")\n",
        "ppc_intervals(\n",
        "  y = data$log_radon,\n",
        "  yrep = posterior_predict(fit),\n",
        "  x = data$county,\n",
        "  prob = 0.8\n",
        ") +\n",
        "  labs(\n",
        "    x = \"County\",\n",
        "    y = \"log radon\",\n",
        "    title = \"80% posterior predictive intervals \\nvs observed log radon\",\n",
        "    subtitle = \"by county\"\n",
        "  ) +\n",
        "  panel_bg(fill = \"gray95\", color = NA) +\n",
        "  grid_lines(color = \"white\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h9HtqG65x1a6"
      },
      "outputs": [],
      "source": [
        "# Write the posterior samples (4000 for each variable) to a CSV.\n",
        "write.csv(tidy(as.matrix(fit)), \"/tmp/radon/stan_fit.csv\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FedP5SMQ3u4z"
      },
      "source": [
        "**Note: Switch back to the Python TF kernel runtime.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wwhJD-t86Dnq"
      },
      "outputs": [],
      "source": [
        "with tf.gfile.Open('/tmp/radon/lme4_fit.csv', 'r') as f:\n",
        "  lme4_fit = pd.read_csv(f, index_col=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Qs9VpUOz6LZR"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div style=\"max-width:1500px;overflow:auto;\">\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>grpvar</th>\n",
              "      <th>term</th>\n",
              "      <th>grp</th>\n",
              "      <th>condval</th>\n",
              "      <th>condsd</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>county</td>\n",
              "      <td>log_uranium_ppm</td>\n",
              "      <td>0</td>\n",
              "      <td>0.667653</td>\n",
              "      <td>0.465584</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>county</td>\n",
              "      <td>log_uranium_ppm</td>\n",
              "      <td>1</td>\n",
              "      <td>0.697805</td>\n",
              "      <td>0.123133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>county</td>\n",
              "      <td>log_uranium_ppm</td>\n",
              "      <td>2</td>\n",
              "      <td>-0.010856</td>\n",
              "      <td>0.847489</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>county</td>\n",
              "      <td>log_uranium_ppm</td>\n",
              "      <td>3</td>\n",
              "      <td>-0.068872</td>\n",
              "      <td>0.422883</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>county</td>\n",
              "      <td>log_uranium_ppm</td>\n",
              "      <td>4</td>\n",
              "      <td>0.036075</td>\n",
              "      <td>0.825677</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   grpvar             term  grp   condval    condsd\n",
              "1  county  log_uranium_ppm    0  0.667653  0.465584\n",
              "2  county  log_uranium_ppm    1  0.697805  0.123133\n",
              "3  county  log_uranium_ppm    2 -0.010856  0.847489\n",
              "4  county  log_uranium_ppm    3 -0.068872  0.422883\n",
              "5  county  log_uranium_ppm    4  0.036075  0.825677"
            ]
          },
          "execution_count": 0,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "lme4_fit.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EJktXVyR6zK6"
      },
      "source": [
        "Retrieve the point estimates and conditional standard deviations for the group random effects from lme4 for visualization later."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "le7XkSvL6a2Z"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(85,) (85,)\n"
          ]
        }
      ],
      "source": [
        "posterior_random_weights_lme4 = np.array(lme4_fit.condval, dtype=np.float32)\n",
        "lme4_prior_scale = np.array(lme4_fit.condsd, dtype=np.float32)\n",
        "print(posterior_random_weights_lme4.shape, lme4_prior_scale.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "42fI-VbduCXy"
      },
      "source": [
        "Draw samples for the county weights using the lme4 estimated means and standard deviations."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "S8TQNRaKFecg"
      },
      "outputs": [],
      "source": [
        "with tf.Session() as sess:\n",
        "  lme4_dist = tfp.distributions.Independent(\n",
        "      tfp.distributions.Normal(\n",
        "          loc=posterior_random_weights_lme4,\n",
        "          scale=lme4_prior_scale),\n",
        "      reinterpreted_batch_ndims=1)\n",
        "  posterior_random_weights_lme4_final_ = sess.run(lme4_dist.sample(4000))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KA2OZUp3FltF"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(4000, 85)"
            ]
          },
          "execution_count": 0,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "posterior_random_weights_lme4_final_.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y8GpeGOauTar"
      },
      "source": [
        "We also retrieve the posterior samples of the county weights from the Stan fit."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YxXhcMfG3uoX"
      },
      "outputs": [],
      "source": [
        "with tf.gfile.Open('/tmp/radon/stan_fit.csv', 'r') as f:\n",
        "  samples = pd.read_csv(f, index_col=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Arc-QdJ33ukk"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div style=\"max-width:1500px;overflow:auto;\">\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>X.Intercept.</th>\n",
              "      <th>floor</th>\n",
              "      <th>b.log_uranium_ppm.county.0.</th>\n",
              "      <th>b.log_uranium_ppm.county.1.</th>\n",
              "      <th>b.log_uranium_ppm.county.2.</th>\n",
              "      <th>b.log_uranium_ppm.county.3.</th>\n",
              "      <th>b.log_uranium_ppm.county.4.</th>\n",
              "      <th>b.log_uranium_ppm.county.5.</th>\n",
              "      <th>b.log_uranium_ppm.county.6.</th>\n",
              "      <th>b.log_uranium_ppm.county.7.</th>\n",
              "      <th>...</th>\n",
              "      <th>b.log_uranium_ppm.county.76.</th>\n",
              "      <th>b.log_uranium_ppm.county.77.</th>\n",
              "      <th>b.log_uranium_ppm.county.78.</th>\n",
              "      <th>b.log_uranium_ppm.county.79.</th>\n",
              "      <th>b.log_uranium_ppm.county.80.</th>\n",
              "      <th>b.log_uranium_ppm.county.81.</th>\n",
              "      <th>b.log_uranium_ppm.county.82.</th>\n",
              "      <th>b.log_uranium_ppm.county.83.</th>\n",
              "      <th>b.log_uranium_ppm.county.84.</th>\n",
              "      <th>sigma</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.450331</td>\n",
              "      <td>-0.757375</td>\n",
              "      <td>1.093099</td>\n",
              "      <td>0.603985</td>\n",
              "      <td>-0.661680</td>\n",
              "      <td>-0.667052</td>\n",
              "      <td>0.549178</td>\n",
              "      <td>-0.150145</td>\n",
              "      <td>1.486173</td>\n",
              "      <td>0.053578</td>\n",
              "      <td>...</td>\n",
              "      <td>0.323107</td>\n",
              "      <td>0.282121</td>\n",
              "      <td>-0.188251</td>\n",
              "      <td>0.889385</td>\n",
              "      <td>1.008348</td>\n",
              "      <td>0.429430</td>\n",
              "      <td>0.404690</td>\n",
              "      <td>0.112424</td>\n",
              "      <td>-0.071612</td>\n",
              "      <td>0.778706</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.453383</td>\n",
              "      <td>-0.659085</td>\n",
              "      <td>0.560266</td>\n",
              "      <td>0.580569</td>\n",
              "      <td>0.419441</td>\n",
              "      <td>-0.154925</td>\n",
              "      <td>-1.263534</td>\n",
              "      <td>0.166291</td>\n",
              "      <td>1.534917</td>\n",
              "      <td>1.141392</td>\n",
              "      <td>...</td>\n",
              "      <td>0.306941</td>\n",
              "      <td>0.331696</td>\n",
              "      <td>0.614852</td>\n",
              "      <td>0.153913</td>\n",
              "      <td>1.253688</td>\n",
              "      <td>0.246746</td>\n",
              "      <td>0.849947</td>\n",
              "      <td>-1.399027</td>\n",
              "      <td>-1.697360</td>\n",
              "      <td>0.754960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.460351</td>\n",
              "      <td>-0.804513</td>\n",
              "      <td>1.145694</td>\n",
              "      <td>0.705337</td>\n",
              "      <td>2.216364</td>\n",
              "      <td>-1.044031</td>\n",
              "      <td>1.510397</td>\n",
              "      <td>-0.472675</td>\n",
              "      <td>0.427448</td>\n",
              "      <td>0.940869</td>\n",
              "      <td>...</td>\n",
              "      <td>1.941637</td>\n",
              "      <td>0.188311</td>\n",
              "      <td>-0.690044</td>\n",
              "      <td>0.946370</td>\n",
              "      <td>1.626093</td>\n",
              "      <td>0.930756</td>\n",
              "      <td>0.141125</td>\n",
              "      <td>0.041392</td>\n",
              "      <td>-0.135538</td>\n",
              "      <td>0.763605</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.438725</td>\n",
              "      <td>-0.565721</td>\n",
              "      <td>0.204148</td>\n",
              "      <td>0.810580</td>\n",
              "      <td>-1.982466</td>\n",
              "      <td>1.044825</td>\n",
              "      <td>-1.339406</td>\n",
              "      <td>0.643384</td>\n",
              "      <td>2.379191</td>\n",
              "      <td>0.295981</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.832814</td>\n",
              "      <td>0.246694</td>\n",
              "      <td>-0.801078</td>\n",
              "      <td>0.072430</td>\n",
              "      <td>0.108882</td>\n",
              "      <td>-0.203805</td>\n",
              "      <td>0.443365</td>\n",
              "      <td>-0.901578</td>\n",
              "      <td>-0.063022</td>\n",
              "      <td>0.750404</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1.457569</td>\n",
              "      <td>-0.658910</td>\n",
              "      <td>0.140554</td>\n",
              "      <td>0.593056</td>\n",
              "      <td>-2.618940</td>\n",
              "      <td>0.484812</td>\n",
              "      <td>-0.417834</td>\n",
              "      <td>0.735558</td>\n",
              "      <td>1.693997</td>\n",
              "      <td>0.230496</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.608215</td>\n",
              "      <td>-0.586245</td>\n",
              "      <td>0.204547</td>\n",
              "      <td>0.350195</td>\n",
              "      <td>0.164781</td>\n",
              "      <td>-0.009945</td>\n",
              "      <td>0.023533</td>\n",
              "      <td>-0.177017</td>\n",
              "      <td>-0.425871</td>\n",
              "      <td>0.755898</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 88 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   X.Intercept.     floor  b.log_uranium_ppm.county.0.  \\\n",
              "1      1.450331 -0.757375                     1.093099   \n",
              "2      1.453383 -0.659085                     0.560266   \n",
              "3      1.460351 -0.804513                     1.145694   \n",
              "4      1.438725 -0.565721                     0.204148   \n",
              "5      1.457569 -0.658910                     0.140554   \n",
              "\n",
              "   b.log_uranium_ppm.county.1.  b.log_uranium_ppm.county.2.  \\\n",
              "1                     0.603985                    -0.661680   \n",
              "2                     0.580569                     0.419441   \n",
              "3                     0.705337                     2.216364   \n",
              "4                     0.810580                    -1.982466   \n",
              "5                     0.593056                    -2.618940   \n",
              "\n",
              "   b.log_uranium_ppm.county.3.  b.log_uranium_ppm.county.4.  \\\n",
              "1                    -0.667052                     0.549178   \n",
              "2                    -0.154925                    -1.263534   \n",
              "3                    -1.044031                     1.510397   \n",
              "4                     1.044825                    -1.339406   \n",
              "5                     0.484812                    -0.417834   \n",
              "\n",
              "   b.log_uranium_ppm.county.5.  b.log_uranium_ppm.county.6.  \\\n",
              "1                    -0.150145                     1.486173   \n",
              "2                     0.166291                     1.534917   \n",
              "3                    -0.472675                     0.427448   \n",
              "4                     0.643384                     2.379191   \n",
              "5                     0.735558                     1.693997   \n",
              "\n",
              "   b.log_uranium_ppm.county.7.    ...     b.log_uranium_ppm.county.76.  \\\n",
              "1                     0.053578    ...                         0.323107   \n",
              "2                     1.141392    ...                         0.306941   \n",
              "3                     0.940869    ...                         1.941637   \n",
              "4                     0.295981    ...                        -0.832814   \n",
              "5                     0.230496    ...                        -0.608215   \n",
              "\n",
              "   b.log_uranium_ppm.county.77.  b.log_uranium_ppm.county.78.  \\\n",
              "1                      0.282121                     -0.188251   \n",
              "2                      0.331696                      0.614852   \n",
              "3                      0.188311                     -0.690044   \n",
              "4                      0.246694                     -0.801078   \n",
              "5                     -0.586245                      0.204547   \n",
              "\n",
              "   b.log_uranium_ppm.county.79.  b.log_uranium_ppm.county.80.  \\\n",
              "1                      0.889385                      1.008348   \n",
              "2                      0.153913                      1.253688   \n",
              "3                      0.946370                      1.626093   \n",
              "4                      0.072430                      0.108882   \n",
              "5                      0.350195                      0.164781   \n",
              "\n",
              "   b.log_uranium_ppm.county.81.  b.log_uranium_ppm.county.82.  \\\n",
              "1                      0.429430                      0.404690   \n",
              "2                      0.246746                      0.849947   \n",
              "3                      0.930756                      0.141125   \n",
              "4                     -0.203805                      0.443365   \n",
              "5                     -0.009945                      0.023533   \n",
              "\n",
              "   b.log_uranium_ppm.county.83.  b.log_uranium_ppm.county.84.     sigma  \n",
              "1                      0.112424                     -0.071612  0.778706  \n",
              "2                     -1.399027                     -1.697360  0.754960  \n",
              "3                      0.041392                     -0.135538  0.763605  \n",
              "4                     -0.901578                     -0.063022  0.750404  \n",
              "5                     -0.177017                     -0.425871  0.755898  \n",
              "\n",
              "[5 rows x 88 columns]"
            ]
          },
          "execution_count": 0,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "samples.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-TdxDTcJ40QY"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(4000, 85)\n"
          ]
        }
      ],
      "source": [
        "posterior_random_weights_cols = [\n",
        "    col for col in samples.columns if 'b.log_uranium_ppm.county' in col\n",
        "]\n",
        "posterior_random_weights_final_stan = samples[\n",
        "    posterior_random_weights_cols].values\n",
        "print(posterior_random_weights_final_stan.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qv_caP1t4FVF"
      },
      "source": [
        " [This Stan example](https://github.com/stan-dev/example-models/blob/master/ARM/Ch.16/radon.3.stan) shows how one would implement LMER in a style closer to TFP, i.e., by directly specifying the probabilistic model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QkchUh3V382r"
      },
      "source": [
        "## 6  HLM In TF Probability"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ywj6S9iQ0aZe"
      },
      "source": [
        "In this section we will use low-level TensorFlow Probability primitives (`Distributions`) to specify our Hierarchical Linear Model as well as fit the unkown parameters."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TOh_69los9gK"
      },
      "outputs": [],
      "source": [
        "# Handy snippet to reset the global graph and global session.\n",
        "with warnings.catch_warnings():\n",
        "  warnings.simplefilter('ignore')\n",
        "  tf.reset_default_graph()\n",
        "  try:\n",
        "    sess.close()\n",
        "  except:\n",
        "    pass\n",
        "  sess = tf.InteractiveSession()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g6xl7I6XTTg5"
      },
      "source": [
        "### 6.1  Specify Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GjzA6-vAXXLS"
      },
      "source": [
        "In this section we specify the [radon linear mixed-effect model](#scrollTo=IFC9r-h0XlQ3) using TFP primitives. To do this, we specify two functions which produce two TFP distributions:\n",
        "- `make_weights_prior`: A multivariate Normal prior for the random weights (which are multiplied by $\\log(\\text{UraniumPPM}_{c_i})$ to compue the linear predictor).\n",
        "- `make_log_radon_likelihood`: A batch of `Normal` distributions over each observed $\\log(\\text{Radon}_i)$ dependent variable.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_QtzWC-ZZ9U-"
      },
      "source": [
        "Since we will be fitting the parameters of each of these distributions we must use TF variables (i.e., [`tf.get_variable`](https://www.tensorflow.org/api_docs/python/tf/get_variable)). However, since we wish to use unconstrained optimzation we must find a way to constrain real-values to achieve the necessary semantics, eg, postives which represent standard deviations. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NzpFXkvOXMav"
      },
      "outputs": [],
      "source": [
        "inv_scale_transform = lambda y: np.log(y)  # Not using TF here.\n",
        "fwd_scale_transform = tf.exp"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "817V1h2_aCFp"
      },
      "source": [
        "The following function constructs our prior, $p(\\beta|\\sigma_C)$ where $\\beta$ denotes the random-effect weights and $\\sigma_C$ the standard deviation.\n",
        "\n",
        "We use `tf.make_template` to ensure that the first call to this function instantiates the TF variables it uses and all subsequent calls _reuse_ the variable's current value."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JnPFL-pKXMRl"
      },
      "outputs": [],
      "source": [
        "def _make_weights_prior(num_counties, dtype):\n",
        "  \"\"\"Returns a `len(log_uranium_ppm)` batch of univariate Normal.\"\"\"\n",
        "  raw_prior_scale = tf.get_variable(\n",
        "      name='raw_prior_scale',\n",
        "      initializer=np.array(inv_scale_transform(1.), dtype=dtype))\n",
        "  return tfp.distributions.Independent(\n",
        "      tfp.distributions.Normal(\n",
        "          loc=tf.zeros(num_counties, dtype=dtype),\n",
        "          scale=fwd_scale_transform(raw_prior_scale)),\n",
        "      reinterpreted_batch_ndims=1)\n",
        "\n",
        "\n",
        "make_weights_prior = tf.make_template(\n",
        "    name_='make_weights_prior', func_=_make_weights_prior)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3-aeEIbmaJQ1"
      },
      "source": [
        "The following function constructs our likelihood, $p(y|x,\\omega,\\beta,\\sigma_N)$ where $y,x$ denote response and evidence, $\\omega,\\beta$ denote fixed- and random-effect weights, and $\\sigma_N$ the standard deviation.\n",
        "\n",
        "Here again we use `tf.make_template` to ensure the TF variables are reused across calls."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wNQTcHcQXMIp"
      },
      "outputs": [],
      "source": [
        "def _make_log_radon_likelihood(random_effect_weights, floor, county,\n",
        "                               log_county_uranium_ppm, init_log_radon_stddev):\n",
        "  raw_likelihood_scale = tf.get_variable(\n",
        "      name='raw_likelihood_scale',\n",
        "      initializer=np.array(\n",
        "          inv_scale_transform(init_log_radon_stddev), dtype=dtype))\n",
        "  fixed_effect_weights = tf.get_variable(\n",
        "      name='fixed_effect_weights', initializer=np.array([0., 1.], dtype=dtype))\n",
        "  fixed_effects = fixed_effect_weights[0] + fixed_effect_weights[1] * floor\n",
        "  random_effects = tf.gather(\n",
        "      random_effect_weights * log_county_uranium_ppm,\n",
        "      indices=tf.to_int32(county),\n",
        "      axis=-1)\n",
        "  linear_predictor = fixed_effects + random_effects\n",
        "  return tfp.distributions.Normal(\n",
        "      loc=linear_predictor, scale=fwd_scale_transform(raw_likelihood_scale))\n",
        "\n",
        "\n",
        "make_log_radon_likelihood = tf.make_template(\n",
        "    name_='make_log_radon_likelihood', func_=_make_log_radon_likelihood)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_dGxuoLPbRMs"
      },
      "source": [
        "Finally we use the prior and likelihood generators to construct the joint log-density."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_UBayNK538JD"
      },
      "outputs": [],
      "source": [
        "def joint_log_prob(random_effect_weights, log_radon, floor, county,\n",
        "                   log_county_uranium_ppm, dtype):\n",
        "  num_counties = len(log_county_uranium_ppm)\n",
        "  rv_weights = make_weights_prior(num_counties, dtype)\n",
        "  rv_radon = make_log_radon_likelihood(\n",
        "      random_effect_weights,\n",
        "      floor,\n",
        "      county,\n",
        "      log_county_uranium_ppm,\n",
        "      init_log_radon_stddev=radon.log_radon.values.std())\n",
        "  return (rv_weights.log_prob(random_effect_weights)\n",
        "          + tf.reduce_sum(rv_radon.log_prob(log_radon), axis=-1))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xkeKH0rTTWDo"
      },
      "source": [
        "### 6.2  Training (Stochastic Approximation of Expectation Maximization)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h7Xr0X4Qbe9C"
      },
      "source": [
        "To fit our linear mixed-effect regression model, we will use a stochastic approximation version of the Expectation Maximization algorithm  (SAEM).  The basic idea is to use samples from the posterior to approximate the expected joint log-density (E-step). Then we find the parameters which maximize this calculation (M-step). Somewhat more concretely, the fixed-point iteration is given by:\n",
        "\n",
        "$$\\begin{align*}\n",
        "\\text{E}[ \\log p(x, Z | \\theta) | \\theta_0]\n",
        "&\\approx \\frac{1}{M} \\sum_{m=1}^M \\log p(x, z_m | \\theta), \\quad Z_m\\sim p(Z | x, \\theta_0) && \\text{E-step}\\\\\n",
        "&=: Q_M(\\theta, \\theta_0) \\\\\n",
        "\\theta_0 &= \\theta_0 - \\eta \\left.\\nabla_\\theta Q_M(\\theta, \\theta_0)\\right|_{\\theta=\\theta_0} && \\text{M-step}\n",
        "\\end{align*}$$\n",
        "\n",
        "where $x$ denotes evidence, $Z$ some latent variable which needs to be marginalized out, and $\\theta,\\theta_0$ possible parameterizations.\n",
        "\n",
        "For a more thorough explanation, see [_Convergence of a stochastic approximation version of the EM algorithms_ by Bernard Delyon, Marc Lavielle, Eric, Moulines (Ann. Statist., 1999)](https://projecteuclid.org/euclid.aos/1018031103)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hwEoELEueaeZ"
      },
      "source": [
        "To compute the E-step, we need to sample from the posterior. Since our posterior is not easy to sample from, we use Hamiltonian Monte Carlo (HMC). HMC is a Monte Carlo Markov Chain procedure which uses gradients (wrt state, not parameters) of the unnormalized posterior log-density to propose new samples.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JOTzK4qne9Qr"
      },
      "source": [
        "Specifying the unnormalized posterior log-density is simple--it is merely the joint log-density \"pinned\" at whatever we wish to condition on."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FSwVJAkNEx6Y"
      },
      "outputs": [],
      "source": [
        "# Specify unnormalized posterior.\n",
        "\n",
        "dtype = np.float32\n",
        "\n",
        "log_county_uranium_ppm = radon[\n",
        "    ['county', 'log_uranium_ppm']].drop_duplicates().values[:, 1]\n",
        "log_county_uranium_ppm = log_county_uranium_ppm.astype(dtype)\n",
        "\n",
        "def unnormalized_posterior_log_prob(random_effect_weights):\n",
        "  return joint_log_prob(\n",
        "      random_effect_weights=random_effect_weights,\n",
        "      log_radon=dtype(radon.log_radon.values),\n",
        "      floor=dtype(radon.floor.values),\n",
        "      county=np.int32(radon.county.values),\n",
        "      log_county_uranium_ppm=log_county_uranium_ppm,\n",
        "      dtype=dtype)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "khZHTgVYfASP"
      },
      "source": [
        "We now complete the E-step setup by creating an HMC transition kernel. \n",
        "\n",
        "Notes:\n",
        "\n",
        "- We use `state_stop_gradient=True`to prevent the M-step from backpropping through draws from the MCMC. (Recall, we needn't backprop through because our E-step is intentionally parameterized at the _previous_ best known estimators.)\n",
        "\n",
        "- We use [`tf.placeholder`](https://www.tensorflow.org/api_docs/python/tf/placeholder) so that when we eventually execute our TF graph, we can feed the previous iteration's random MCMC sample as the the next iteration's chain's value.\n",
        "\n",
        "- We use TFP's adaptive `step_size` heuristic, `tfp.mcmc.hmc_step_size_update_fn`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WnZ_KMP0E0ot"
      },
      "outputs": [],
      "source": [
        "# Set-up E-step.\n",
        "\n",
        "step_size = tf.get_variable(\n",
        "    'step_size',\n",
        "    initializer=np.array(0.2, dtype=dtype),\n",
        "    trainable=False)\n",
        "\n",
        "hmc = tfp.mcmc.HamiltonianMonteCarlo(\n",
        "    target_log_prob_fn=unnormalized_posterior_log_prob,\n",
        "    num_leapfrog_steps=2,\n",
        "    step_size=step_size,\n",
        "    step_size_update_fn=tfp.mcmc.make_simple_step_size_update_policy(\n",
        "      num_adaptation_steps=None),\n",
        "    state_gradients_are_stopped=True)\n",
        "\n",
        "init_random_weights = tf.placeholder(dtype, shape=[len(log_county_uranium_ppm)])\n",
        "\n",
        "posterior_random_weights, kernel_results = tfp.mcmc.sample_chain(\n",
        "    num_results=3,\n",
        "    num_burnin_steps=0,\n",
        "    num_steps_between_results=0,\n",
        "    current_state=init_random_weights,\n",
        "    kernel=hmc)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-GmtUwoLff1y"
      },
      "source": [
        "We now set-up the M-step. This is essentially the same as an optimization one might do in TF."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wceMwnGwvUfF"
      },
      "outputs": [],
      "source": [
        "# Set-up M-step.\n",
        "\n",
        "loss = -tf.reduce_mean(kernel_results.accepted_results.target_log_prob)\n",
        "\n",
        "global_step = tf.train.get_or_create_global_step()\n",
        "\n",
        "learning_rate = tf.train.exponential_decay(\n",
        "    learning_rate=0.1,\n",
        "    global_step=global_step,\n",
        "    decay_steps=2,\n",
        "    decay_rate=0.99)\n",
        "\n",
        "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n",
        "train_op = optimizer.minimize(loss, global_step=global_step)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s_ykFN0Cfoel"
      },
      "source": [
        "We conclude with some housekeeping tasks. We must tell TF that all variables are initialized. We also create handles to our TF variables so we can `print` their values at each iteration of the procedure. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PakV59O8E3m5"
      },
      "outputs": [],
      "source": [
        "# Initialize all variables.\n",
        "\n",
        "init_op = tf.initialize_all_variables()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FziBCkW_NXFF"
      },
      "outputs": [],
      "source": [
        "# Grab variable handles for diagnostic purposes.\n",
        "\n",
        "with tf.variable_scope('make_weights_prior', reuse=True):\n",
        "  prior_scale = fwd_scale_transform(tf.get_variable(\n",
        "      name='raw_prior_scale', dtype=dtype))\n",
        "  \n",
        "with tf.variable_scope('make_log_radon_likelihood', reuse=True):\n",
        "  likelihood_scale = fwd_scale_transform(tf.get_variable(\n",
        "      name='raw_likelihood_scale', dtype=dtype))\n",
        "  fixed_effect_weights = tf.get_variable(\n",
        "      name='fixed_effect_weights', dtype=dtype)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pwjiJLywlZgF"
      },
      "source": [
        "### 6.3  Execute"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ouO-E4Ncf0KE"
      },
      "source": [
        "In this section we execute our SAEM TF graph.  The main trick here is to feed our last draw from the HMC kernel into the next iteration. This is achieved through our use of `feed_dict` in the `sess.run` call."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Cy36-LMMNbTc"
      },
      "outputs": [],
      "source": [
        "init_op.run()\n",
        "w_ = np.zeros([len(log_county_uranium_ppm)], dtype=dtype)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OrzwMVaoE0i2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "global_step:   0  loss: 1966.948  acceptance:1.0000  step_size:0.2000  prior_scale:1.0000  likelihood_scale:0.8529  fixed_effect_weights:[ 0.  1.]\n",
            "global_step: 100  loss: 1165.385  acceptance:0.6205  step_size:0.2040  prior_scale:0.9568  likelihood_scale:0.7611  fixed_effect_weights:[ 1.47523439 -0.66043079]\n",
            "global_step: 200  loss: 1149.851  acceptance:0.6766  step_size:0.2081  prior_scale:0.7465  likelihood_scale:0.7665  fixed_effect_weights:[ 1.48918796 -0.67058587]\n",
            "global_step: 300  loss: 1163.464  acceptance:0.6811  step_size:0.2040  prior_scale:0.8445  likelihood_scale:0.7594  fixed_effect_weights:[ 1.46291411 -0.67586178]\n",
            "global_step: 400  loss: 1158.846  acceptance:0.6808  step_size:0.2081  prior_scale:0.8377  likelihood_scale:0.7574  fixed_effect_weights:[ 1.47349834 -0.68823022]\n",
            "global_step: 500  loss: 1154.193  acceptance:0.6766  step_size:0.1961  prior_scale:0.8546  likelihood_scale:0.7564  fixed_effect_weights:[ 1.47703862 -0.67521363]\n",
            "global_step: 600  loss: 1163.903  acceptance:0.6783  step_size:0.2040  prior_scale:0.9491  likelihood_scale:0.7587  fixed_effect_weights:[ 1.48268366 -0.69667786]\n",
            "global_step: 700  loss: 1163.894  acceptance:0.6767  step_size:0.1961  prior_scale:0.8644  likelihood_scale:0.7617  fixed_effect_weights:[ 1.4719094  -0.66897118]\n",
            "global_step: 800  loss: 1153.689  acceptance:0.6742  step_size:0.2123  prior_scale:0.8366  likelihood_scale:0.7609  fixed_effect_weights:[ 1.47345769 -0.68343043]\n",
            "global_step: 900  loss: 1155.312  acceptance:0.6718  step_size:0.2040  prior_scale:0.8633  likelihood_scale:0.7581  fixed_effect_weights:[ 1.47426116 -0.6748783 ]\n",
            "global_step:1000  loss: 1151.278  acceptance:0.6690  step_size:0.2081  prior_scale:0.8737  likelihood_scale:0.7581  fixed_effect_weights:[ 1.46990883 -0.68891817]\n",
            "global_step:1100  loss: 1156.858  acceptance:0.6676  step_size:0.2040  prior_scale:0.8716  likelihood_scale:0.7584  fixed_effect_weights:[ 1.47386014 -0.6796245 ]\n",
            "global_step:1200  loss: 1166.247  acceptance:0.6653  step_size:0.2000  prior_scale:0.8748  likelihood_scale:0.7588  fixed_effect_weights:[ 1.47389269 -0.67626756]\n",
            "global_step:1300  loss: 1165.263  acceptance:0.6636  step_size:0.2040  prior_scale:0.8771  likelihood_scale:0.7581  fixed_effect_weights:[ 1.47612262 -0.67752427]\n",
            "global_step:1400  loss: 1158.108  acceptance:0.6640  step_size:0.2040  prior_scale:0.8748  likelihood_scale:0.7587  fixed_effect_weights:[ 1.47534692 -0.6789524 ]\n",
            "global_step:1499  loss: 1161.030  acceptance:0.6638  step_size:0.1941  prior_scale:0.8738  likelihood_scale:0.7589  fixed_effect_weights:[ 1.47624075 -0.67875224]\n",
            "CPU times: user 1min 16s, sys: 17.6 s, total: 1min 33s\n",
            "Wall time: 27.9 s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "maxiter = int(1500)\n",
        "num_accepted = 0\n",
        "num_drawn = 0\n",
        "for i in range(maxiter):\n",
        "  [\n",
        "      _,\n",
        "      global_step_,\n",
        "      loss_,\n",
        "      posterior_random_weights_,\n",
        "      kernel_results_,\n",
        "      step_size_,\n",
        "      prior_scale_,\n",
        "      likelihood_scale_,\n",
        "      fixed_effect_weights_,\n",
        "  ] = sess.run([\n",
        "      train_op,\n",
        "      global_step,\n",
        "      loss,\n",
        "      posterior_random_weights,\n",
        "      kernel_results,\n",
        "      step_size,\n",
        "      prior_scale,\n",
        "      likelihood_scale,\n",
        "      fixed_effect_weights,\n",
        "  ], feed_dict={init_random_weights: w_})\n",
        "  w_ = posterior_random_weights_[-1, :]\n",
        "  num_accepted += kernel_results_.is_accepted.sum()\n",
        "  num_drawn += kernel_results_.is_accepted.size\n",
        "  acceptance_rate = num_accepted / num_drawn\n",
        "  if i % 100 == 0 or i == maxiter - 1:\n",
        "    print('global_step:{:>4}  loss:{: 9.3f}  acceptance:{:.4f}  '\n",
        "          'step_size:{:.4f}  prior_scale:{:.4f}  likelihood_scale:{:.4f}  '\n",
        "          'fixed_effect_weights:{}'.format(\n",
        "              global_step_, loss_.mean(), acceptance_rate, step_size_,\n",
        "              prior_scale_, likelihood_scale_, fixed_effect_weights_))              "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QR50yLYygWdg"
      },
      "source": [
        "Looks like after ~1500 steps, our estimates of the parameters have stabilized."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x2BtkWEIVsB9"
      },
      "source": [
        "### 6.4  Results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HYs17VUto_te"
      },
      "source": [
        "Now that we've fit the parameters, let's generate a large number of posterior samples and study the results."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "v-X0DhqHjdue"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CPU times: user 1min 42s, sys: 26.6 s, total: 2min 8s\n",
            "Wall time: 35.1 s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "posterior_random_weights_final, kernel_results_final = tfp.mcmc.sample_chain(\n",
        "    num_results=int(15e3),\n",
        "    num_burnin_steps=int(1e3),\n",
        "    current_state=init_random_weights,\n",
        "    kernel=tfp.mcmc.HamiltonianMonteCarlo(\n",
        "      target_log_prob_fn=unnormalized_posterior_log_prob,\n",
        "      num_leapfrog_steps=2,\n",
        "      step_size=step_size))\n",
        "\n",
        "[\n",
        "    posterior_random_weights_final_,\n",
        "    kernel_results_final_,\n",
        "] = sess.run([\n",
        "    posterior_random_weights_final,\n",
        "    kernel_results_final,\n",
        "], feed_dict={init_random_weights: w_})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LCECfwexk1DN"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "prior_scale:  0.873799\n",
            "likelihood_scale:  0.758913\n",
            "fixed_effect_weights:  [ 1.47624075 -0.67875224]\n",
            "acceptance rate final:  0.7448\n"
          ]
        }
      ],
      "source": [
        "print('prior_scale: ', prior_scale_)\n",
        "print('likelihood_scale: ', likelihood_scale_)\n",
        "print('fixed_effect_weights: ', fixed_effect_weights_)\n",
        "print('acceptance rate final: ', kernel_results_final_.is_accepted.mean())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qbzMVKPykLYq"
      },
      "source": [
        "We now construct a box and whisker diagram of the $\\beta_c \\log(\\text{UraniumPPM}_c)$ random-effect. We'll order the random-effects by decreasing county frequency."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vjA7TVT4wuQa"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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Upaen65BDDtHpp5/epuMRbiwkJSVpx44dOuWU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MWL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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fbc95c5a0d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "x = posterior_random_weights_final_ * log_county_uranium_ppm\n",
        "I = county_freq[:, 0]\n",
        "x = x[:, I]\n",
        "cols = np.array(county_name)[I]\n",
        "pw = pd.DataFrame(x)\n",
        "pw.columns = cols\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(25, 4))\n",
        "ax = pw.boxplot(rot=80, vert=True);"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DXIqPxN1j3wI"
      },
      "source": [
        "From this box and whisker diagram, we observe that the variance of the county-level $\\log(\\text{UraniumPPM})$ random-effect increases as the county is less represented in the dataset. Intutively this makes sense--we should be less certain about the impact of a certain county if we have less evidence for it."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DtlOjtAkyE76"
      },
      "source": [
        "## 7 Side-by-Side-by-Side Comparison"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QF6OwHJSk7xM"
      },
      "source": [
        "We now compare the results of all three procedures. To do this, we will compute non-parameteric estimates of the posterior samples as generated by Stan and TFP. We will also compare against the parameteric (approximate) estimates produced by R's `lme4` package."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qg8pYIRQy9Ea"
      },
      "source": [
        "The following plot depicts the posterior distribution of each weight for each county in Minnesota. We show results for Stan (red), TFP (blue), and R's `lme4` (orange).  We shade results from Stan and TFP thus expect to see purple when the two agree. For simplicity we do not shade results from R.  Each subplot represents a single county and are ordered in descending frequency in raster scan order (i.e., from left-to-right then top-to-bottom)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9vhm-sNWrhmB"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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lypWcOHGCZcuWERAQUGAMP/74I0lJSTz33HNe3SV2797N7Nmzi3ZQPqxZs4Z9+/YxcOBA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\n0el09OrVl6wso3vmB5D6IHy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CTMWYl8NVBZ2mYU/ef50iFEJcD3abiwBdBlZr4QdGhL9a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\nPy5skeDt6aefyfbt2wiFgnXe27WrCFk2kJ6eUev17du3ccEFZzNp0mV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vvvoW7703hXfe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pq7gRi/cT8nLXYuMN2qreIb5zFk7vHFwTJ8Kxk8FUh8RV30HH1KDlI/Sq1zGD2zFiSeKxDCp2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SVhexELE8S2q924ZhsFCKoqJsNg66rLCfgzqX1XPaKBZYDdoXJ635oXjXXBOCCR1m7d0QYO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\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/SGnHkg4kOkgdhEDK7eee3nA+aOEdyB15BCAqSuJy27HkiUACxLuwVXkayqFGB0JoxCh1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\ntFHIz19CTs5YRo3K+dWyXzcSX3HSSady0kmn8OWXc/b4nE16egZDhgw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FF19v85lwm83G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\nBEzTYMuWTc2u/+Uv7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bs9vPLKizz++CO4XE569erDt799HZs2bTxiXfCKK2Y1Obdo2j5m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i+9HbatHDNVixvZCqRrNqEKRxIjEMk0RMpSM8knB7EclIKX73WFSPH6cT8uxAwSlDEQ4UWY6Z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K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\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ZQFJqtR9Koh1JPs6zVI+QUzFr/mIB4utn/uj2Fvv/KCow0XMLkbRYovmpeWMaGjZzIxLB\n6rpVnVPTJF4Pjeq2Qo+3PUYEiIIeRjajYISRiCIZESQzjGSEkQnjVkK4qm8f1Q0DZB3sRvxYWLMQ\n8Wdi6D0IBPoRCnThRM5f0OqYCmasM9CZ8rIp5OSFMWPf43TtwZuSjzelCJvzALK6GkVVkSQJnfjx\njaG7MQwvSC5MyR5/yfGX7nYQDOtIinKofZCVeEeDrOJ2g2qRqst7rb/E/2rpZ4Kv28n+3zkqLdpx\nYDh8hEIRiIXir1ZIM23xPBwDo/pAtipY3hJJOmItkYeWIQEZIGXEW45w9auaphlgRlClAJJZiUkl\nsuJHsYSwOSJIcghV1VDVGIqio6phFNWPqmrIioYi69WbqTnZl2rOaQ5tvvqk51BYnXUSnQGHPtda\n9bA5a4gsSWgp8Rm/CwrOol27dvXWKSsuJhpT0DUFXXei6yq6phKLWYlFLWgxK7pmQ9fsGLoNw3Bg\n6ClIeLFYLSgWEh0hifTYD80bWn3IWP2fHI6/WtipU8Ya11Q9bA15aI6m8iEBTjtgB0ipfkE4ApVB\nhZiuoGmgRaKY0SimFkKS/ShyEFmNoFoiWCzRxEu1aljUCKolhmqJYrVEkZUgiqIj17xkA1nVa1fB\nQ4mp9aGmji2Zex6Tpk9MSnfp8uX4K0Pxul3TiVZNVkvwZL4TX1Yrzpq3ui5hBmVMPV4TGuokM2t1\nQpimiaEr8Zcmoxsqmq6g6VZ03UFMs6DrKpGIk2jYhR5zEjN8RPVMbLIPq2ok4lWcEDYjhEMRNMKt\nrow1VG9aV13xEmYIBIZACUiShsVWjt3lJ6aVoCplWNQqJCmCLMdQ1SiqEkKWjXgZlg1kyUBW9Hg3\nck0hrW476hXqRN+yhFmrcZHqNCCxUgnFnvy1QOllGFq7Qx1itVV3GtcWzUqhQ6fkmdGDua/FJ7KF\nREGvqTO1o0peWH0yp6tomhUtZiWmW9BiLnRNJRqxEYk6iUWdRCNOYrqHmOYlqnlBtqMqMjaLjE01\nsCg6Hg94kubZ8hM4wkPA1lXG4urWldaYh7oay4PTQvwKLF2JGV2JBSEQBK1AQtNlTC2GrFcgUYlE\nOSgBVDWCrOooqhGvZ6qOosQO1TPZQJZ1ZNlEknVkJYakVIdXryPJBnJ1WE3dO3SMVufCTrVV357P\nyDNHHPP/RTgMm7+owL3xawKBIGDWqlYmbm8+/YZ8UP0p6Z9Df0zzUBtWE27GLw6ZgGSa6NVtj65b\n0HSFWNRBLGpH11wouEBKJWb4iER8KEoGoaBWK5WVx5zPlnaqH3tJaldCka748yX2HJDRIwEU8rFa\n87G7KnG6K7E7gtgcVdjtBUhy/FJh/JKhCRIE/AqmYVZfd6zdHsTfR2wSihNqzjGkWssApLxhmOf8\n6wTmuuW06FMVCgsLWyoqQWgRZvVJgWma1XNt1EwEqCctq/uq+W7teOqGNURVVVJrXWJpqONA1BPh\nh65ufTMMg86dOyetU1RUlHhfu2Gu/b6xelkTb+04an+/ZmSToigoipIUVvNeURRkMUxRgEQZbayd\nqFmm6/Gn0dScyNeEA/h8Piy1bodorC1prKzXyMzMTPq8f//+xHo15bd2OW4oDkFoLRqqX7XbjZq/\nNevW6Nu3b4ulobi4mPz8/KSwunWu9vva9a/uq6bdEfWy9aopf7X393XLpNvtxmq1JoXVfBfA5XLh\ndrfO+7Va/HGMgiAIgiAIgiAIgiC0HeJyiiAIgiAIgiAIgiAIjRIdB4IgCIIgCIIgCIIgNEp0HAiC\nIAiCIAiCIAiC0CjRcSAIgiAIgiAIgiAIQqNa9HGMmqZTVhZsyShPuNRUp8jDEbKvHwRAeOjmFouz\nLfwOmZmeBsNFPTkxmiqXrSEPzdEW8tFQXRH15NRxrPk4Hm3EkWoLv0VbrSfQun+fmvLtOH93q81D\njdb8O9QQx16ntrp5OBXahyPVFn6HxupJU1p0xIGqKi0Z3Ukh8nBqaAt5aExbyJvIw6mjreSjrraQ\nr7aQB2gb+WgLeWhIW8lXW8iHyMOprS3kTeTh1NAW8nC0xK0KgiAIgiAIgiAIgiA0qkVvVRB+mFrT\n8CLhh0OUS0E4NYi6KLRlNeX76Ab+CsIPm2gfWhcx4kA4IT788D2efvrxY44nFovx859fSnl5eQuk\nShBar/vv/xNffrnimOMpKyvlqqsuQ9O0FkiVIDSspcprdvYubrzx2hZIkSC0Xt99t5J77rm9ReK6\n/vpfsnfvnhaJSxBa2gsvPMM777zVInGJsn7sflAdB5dd9iPWrFnV6PK8vFz69+/P44//vcHl77zz\nFldf/VMmTRrLJZdM47777mb37uxG4/vqqy+4/vpfMmnSWKZPn8hDD91HcXFRYvns2S/y0EP31vve\n2LEjyck5CMBNN/2WuXM/ajD+BQvmMm7cmUyePI6pU8/j2mt/ztdff1kvT+eeO6rBPI0dO5JJk85l\n8uRxXHLJNJ5++glM00wsb2zb+fl5jB07ksmTxzF58rhEHMuWfdpgOjVN49VXZ3PllVcDUFFRzo03\n/ppp0yZwwQXjufHGa9m0aUNSvgYMGJAU9/r1awGwWCxMmzaD1157pcFtCfU9+OC9/O1vDyaFrVu3\nhmnTJlBaWsLs2S9y3nmjk37PCy4Yn1i3dnmsbcGCucyceV2j261f/u+lqKgQAF3XmTTpXLZv35pY\nf/HiBYwdO7Je2M9/fmmD8f/1r/czduxIvvrqi6TwWbP+ydixI1mwYG4inTX1pHaZKiqK18VLL72I\nCRPGMGXKuOry+Gs+/PC9pLrw8MMP8NJLzyc+a5rGf/7zAldccQmTJp3LZZfN4JFHHiI/Px9Irjvr\n1q1h7NiRPPHEP5LSOXPmdU2msaSkuMG8Z2fvIjt7J+ecMy4R9u67b3HZZTOYOvU8rr/+ajZuXF/v\ne5qmceWVP+GSS6YlwlJT0xg+fAQfffReg9sSToxLL72I8ePPprKyIin8V7+6krFjRzZYtmqsW7cm\n6Tdtat/++9//hvHjxzB58jimT5/En/50O6WlJfXSNH/+J4wdO5KFCxcmwsrKSpk+fWJin1zj4Ycf\n4IEH/txg3hoqr7W/V3cfs2/fXm6++UamTj2PK664hM8//yyxrFev3ng83nptndB6XXrpRQwePLjJ\nsl/jP/95oV5bAY23SbWP/RYsmMvYsSN5883Xkta55JJpSWV6z57d3HXXrUydeh5Tpozj5ptvZPPm\njYnlNcdBv/71L5Liqago57zzRnPZZTOS8jdhwpikffuTTz4KHKpjTaWnrhdffJZf/OJX9cJr2pva\n7dXu3dnceutNTJ8+kXPPHVXvO1de+Qteeum5RrcltG7//e8r3H77zUlhV1zxY+6445Y6YZewdOkS\nIPm4r7FzFah/XvXpp4u44ILxbNiwLlFHDMMADh2zbdq0KbF+Ts5Bxo4d2Wjay8vLWbRoPjNmXJII\nW7p0CVdddRlTpozjF7+4nC+++CyxTJT14+8H1XHQlIUL55GSksLSpYvrXX178slHee+9t/njH+9g\nwYLlvPnm+4wdO45vvmn44GX58k958ME/89OfXsm8eUv573//h6qqzJx5HYGAv9aaUr3vSlL9sMYM\nGnQ6ixevYOHCz5g27Ufcd99dVFZWJuXJ6/U2mCdJkpgz500WL17B00+/wLJlS5g3r+FOiobSuGjR\nZyxevIIlSz5n8eIVjB8/scF1v/jiM7p370F6egYADoeTe+75C/PmLWXBgmVceeXV3HnnrYmdC8Cw\nYcOS4h46dHhi2aRJU1i4cK64QtpMt9xyGytXfs3q1d8BEI1G+cc//spNN91KWlo6ABMmTGbx4hWJ\n//MFC5Ylvn+48tjYsobLv4WZM6/D7/ejKAqDB5/OunWHDow2bFhHt2496oXV/u3rbrtr126Jk2+I\nd0h89tlSOnXqkrRuTT2pXaYyMzMT8Tz66CwWLVrBe+99wlVX/YrXX3+1XmdLbX/60+18/fWXPPDA\nwyxa9Blz5rxB3779WbPmuwbXt9sdLFw4r94BcFNprKkzdX300XtMnnxB4vPWrZt54YVnePjhfyT2\nBffcc3vSySLA66/PSfzmtU2aNJWPPnq/0bQJx58kSXTo0JElSxYlwnbv3kU0GjmiNqEmrsPt2yVJ\n4v/9vztZvHgFb7/9AaFQiGeeebJePDVt4ocffpgIS01N4w9/+H888sj/EY1GAVi9+jtWrvyaP/6x\n4SugdctrjY0b15Obm5OUP13XueuuWxkz5lwWLFjOHXfcw0MP3cvBgwcS60ycOJUPPxQdXW2FJEl0\n7ty52WV/8eIFpKSkJO37a8fVFK/Xy+uvzyEYbHhW9Jycg8yceR29e/fhnXc+4cMPFzJ27Dj++Mff\ns2VL8rDqcDjEnj27E5+XLFlIp06d66Xp0UdnJe3bb7nl9sSyptJT1/btWwkE/PTvPzApXNM0nnrq\nnwwcODgpXFVVJkyYxF133ddgfGPGnMvatWsa7DwUWr+hQ4exadPGxPFAaWkJuq6zY8f2pLDc3IMM\nGxY/3qpfj5quVwsWzOXJJx/lsceeYsiQYfXikSSJlJQUnnwyua05XJ2dP/8TRo8+G6vVCkBxcRH/\n93/38Yc//D8WLVrBzJl/4IEH/pwYhSzK+vEnOg5qWbhwHrfccguqqvLVV58nwg8ePMAHH7zL/fc/\nzLBhZ6CqKjabjUmTpvLzn/+ywbieeWYWv/rV9UycOAWr1Upqahp33XUvdru9ySE3dQ/2m2vatBlE\nIhEOHDh0gLVw4Tyuu+7Genmq2U7Ntjp16szgwUPYufP7Zm+vuelcufLrpJM/q9VKly5dE3FIkozf\nX5XU4XE4mZlZeDxetmzZ1PTKAl5vCrfcchv/+MdfCYfDzJ79Ip07d2Hq1GlNf5mjK4+NlX+Hw8Hb\nb78OwOmnD2XDhtqdBOv5+c+vZv36NUlhQ4Y03HEAcPbZY9m0aQN+f7wz7ttvv6F379NIT69/cnw4\nNXl0Ol2MGTOWBx98mIUL5yUdENZYtepb1qxZxd///jh9+/ZDlmWcThc//vGlTJv2owbj93g8XHDB\nRcye/cIRpasxdetUXl4ePXr04rTT+gIwdep0KisrKCsrTayTm5vDkiWL+MUvrqkX34ABg8jNzaGg\noPGODeH4mzLlQhYuPHQytGDBPC64YPoRx9OcfXvNcpfLzdix59Vbnp+fx4YN67j99j/x5ZdfUlZW\nllg2efIFdOvWjZdeep5IJMJjj/2NW265Ha83pcH01C2vEO8gePLJR7n11juS9jH79u2lpKSEyy//\nGZIkMXz4CAYPHsKiRfMT6wwffgZr1nwnOo/bkBkzZjSr7K9fv5aSkmL+8Ifb+PTTRUdVBrp168Gg\nQYMTbVFds2e/wODBp3PddTfg8XhwOBxceukVTJlyIc8991TSulOmXMiCBZ8kPi9cOL/BtvVw7WhT\n6akrXp/OqBf+1luvMWrUWXTt2i0pvGvXbkyb9iN69OjZYHxWq5W+ffvx3Xcrm7V9oXXp338gmhZj\n584dAKxfv45hw86ga9duSWEdO3ZOXFg40uO+jz56n2eemcXjj/+LgQMHNbre1KnT2bFjBxs2rGtW\nvN9+m1zWCwsL8Hi8jBo1GoCzzjoHu92RGB0hyvrxJzoOqm3YsI6ioiKmTZvG+edPZOHCeYllq1d/\nR1ZWO/r169+suPbv30thYQHnnz8hKVySJMaNG39cCqymaXz88Qc4nU66dYs3GjV5mjhxSr081bVv\n3142bFhX70rt4TR3x7J79656DRnAL3/5M8aPP5t77rmNiy66GJ/Pl1i2detWpk+fxJVX/oRXXnkp\naTQCQLdu3dm1q/mdHD90558/kb59+3P//fcwd+6H3HHHn47btpoq/6tXfwvA0KHDE7eolJeXE4mE\nGT9+Etu2bU2E7d+/l6FDhzW6LZvNxjnnnMunn8avVC1cOI+pU6cddedbjf79B5KZmdVg47ZmzSr6\n9x9IRkbmEcX5y19ey4oVyzhwYP8xpS0cDpOXl5tUp84662wMw2Dr1s0YhsHcuR/Su3efpNEFTz75\nGDfc8LtEz31tiqLQqVMXdu3aeUxpE47NwIGDCQaD7N+/F8MwWLZsCZMnX3BM5bmpfXtFRTkrViyj\nc+euSeELF86jb9/+jBt3Pj169GDJkgVJy2+77W7mzfuI+++/h549ezc64qyh8grw9tuvM2zYGfTs\n2bvON+rn1TRNdu/elfickZGJqqrs37+3kVwLrc2QIUOaVfYXLpzHmDFjE+XtaG5ZkSSJ6667kbff\nfoOqqqp6y1ev/o7zz69fnsePn8imTRuIRCKJeCZPvpBPP12MaZpkZ2cTCgXrjQQ41vTUlZ1d/5gq\nPz+P+fM/4Zprrj+ibdfo1q2H2P+3UaqqMmDAINavjx/PbNiwlqFDh3P66UPrhDV+rHU4H3zwDrNn\nv8BTTz1Hnz79Druu3W7nhhtu4IUXnmlW3HXLer9+A+jWrTtffvk5hmHw+eefYbVa6d27bjvSOFHW\nj414qkK1hQvncdZZZ+PxeJg4cSo33fQbysvL8fl8VFZWNDpkuCE1Q2Ya+k56egbl5WX1wo/W5s0b\nueCC8SiKQufOXfjb3/6J2+0mFKpK5MntdtfLU41f//oqdF0nHA4zceIULrnksmZt1zRNpk+fFP+g\nVSBJ8PxL79G1a/d661ZV+XE6XfXC58x5k1gsxuefLycWiyXChw4dzty5c7Favezenc19992Nqqpc\nddWvEus4na5mNbDCIbfeegeXX34xN9zwOzIzs5KWLVu2JOkArE+fvsyadXT3gTVd/uPLBwwYRDgc\nJjt7Fzk5Bzn99CHYbDY6duyUCGvfvgNZWe0Ou70pUy7k2WefYtKkKWzYsJY///kB3nvvf1j234N9\n/V3AI4l6AvGy6/P5WLq04Tk5amRkZFJVVX8UTEXFke0PaqSmpjFjxk946aXneeCBh+stbyiNb731\nQb31/P4qJElKqlNOp4tx485P3N/rdnt47LFDV8ZWrFiOYeicc8441q1bUy/OeBxO/H5Rp062+BXM\neQwdOpxu3bofcQdVjab27bNmPca//vUkgYCf007rwz33/CVp+cKF87n00ssBuOiii/j440+4/PIr\nE8szM7P49a9v4LnnnuZ///uQxvj9VUgYpO08B31EfJh3QUE+H3/8IbNnv1Zv/a5du5Oamsobb/yX\nyy//GWvXrmb9+rUMH558H2y8DfDX+77QejVV9iORMMuXf8q99z6Eqqqcd94EFiyYy7nnnpdYp/Z+\nFOL70lCo/i0AvXufxqhRo3n99TnccMPvk5aVl5c3uI/PyMjANM2kY4+srCx6pOWy4a0hbIz9vtGR\nfHfffRuKolSPspT43e/+wPTpFzcrPXX5/VU4nc6ksFmzHuP662/Ebrcf9ruNcTqdYvh2GzZ06HA2\nbFjL5Zf/jA0b1nP55VeSnp7Bxx+/nwi74oqfH1Xcq1d/x7BhIxroBG7Y5Zdfzosv/ptvv/2Gzp27\ngKljXz+owacr1C3rsiwzZcqFPPDAn4lGI1itVh588BFstuaXe1HWj40YcQBEIhGWL/+USZOmAjBo\n0GCystqxZEl8QiivN6XRScoaUnNi3tB3SkqK8flSgfhVvrrD7Go+q2rz+nQGDTqdBQuWMXfuEp5/\nfjbDh49oVp5qzJ79OkuWfMGDD/6NrVs3EwqFmrVdSZKYPz8+R8HyRyMs+0ekwU4DiA/TDgYDDS6z\nWCxMmDCZ1157hezs+BWlDh060qlTJwB69uzFNddcx2efLUv6XjAYwOMRDz86Eqmpafh8Prp3rz+E\na/z4SSxYsCzxOtpOA2hO+Y8vt1qt9O8/kPXr17Bhw1pOPz3e2z148JBEWGPzG9R2+ulDKS8vY86c\n/3D22WMbvKJeU08WLFjGwoXLGzwhr6uoqBCPx1svPCXlyPYHtV111S/57ruVDfZ2NzeNbne83Neu\nUx9//AHz5n3C66+/y2efreTeex/kjjtuoaSkmHA4zHPPPc0f/3gH0PhIoWAwmIhbOHkmT76QJUsW\nMn/+Jw2ehDTWbtRtM5rat998820sXLicOXPeoqqqisLCwsSyjRvXk5eXw4QJkwGYNm0a2dm76pXb\nHj164vF4SE1NazQ/NWUqED4U9vTTj3PNNdfVO/mBeNv3t789xtdff8HFF0/l7bffYPz4SWRlJXd2\nxtsAd6PbFVqfpsr+ihXLUVWV0aPPBuJzs6xc+RUVFYeeslR7P1qzL22s8/m6637Lhx++W+8kwufz\nNbiPLy4uRpKkesceF47S+WSlwrx58xqcywPgkUf+mUjPggXLkjoNmkpPXR6PN2k+hC+//JxgMNjg\nKInmEvv/tm3o0OFs3LiBqqoqKirKq29hO53NmzdSVVXFnj3ZzTreashtt93NgQP7DzsvVG1Wq5Vf\n/eo6XnrpuSZH09Ut66tWfctzzz3FM8+8yIoV3/L00y/wyCMPHdEIAlHWj43oOAA+/3w5gUCAf/7z\n75xzzjnMmDGF4uKixND+ESNGUVhYwI4d25sVX9eu3cnMzKr3lAHTNFmxYhnDhsXv12nXrj35+XlJ\n6+Tm5qAoSr0rwseSpxkzptTLU+00QXwo+8CBg3n55RebvY3mDp/t3fu0Jodna5pGbm79mfsb29be\nvXvp3btPs7YvnFhNlf8RI85MhA0ZMoz169exceP6xGQ6Q4bEh8/F5zdo3tC5yZMv4O2332Dq1CO/\nH7wh27ZtoaSkuMHtjxgxim3btiQ9IaW5vN4ULr/8Z7z00nNHPOFdDbvdTseOnZPqVHb2TsaMGZuY\nlOvMM88iPT2dzZs3cuDAfgoK8pg58zpmzJjCn/98JyUlxcyYMTUxWaOu6+TkHKB379OOKk1Cy2nf\nvj0dOnTk22+/Zty48+stb6zdaN++Q1JYc/ftPXv24uqrr+Xxxx9JhC1YEG8nfvWrK5kxYwo//elP\nkSTpsLe7NcZut9M5w2R/4aHyvnr1Kp59dlaibQK44YZrE7ccJWzpzQAAIABJREFU9ezZm3/960Xm\nzv2Uf/7zKXJzDyYN/y4uLkbTtEY7q4XWqamyv3DhPEKhED/5yXRmzJjCfffdja7riXJzpLp27c65\n557Pq6++nBQ+YsQoli+vPyJt2bIlDBp0OjabLSl8wlCDL7fIdO3alXbt2je4reYcLzWWnrp69erN\ngQP7Ep/Xrl3Fjh3bEvVp6dLF/O9/b3L33bc1uc0a+/btEfv/NmzgwMH4/VV8/PH7DB48BIiP2kpP\nz+Tjj98nIyOzXhvSXKmpacya9SwbNqznscceafoLwIUXXoTf7+fzz5cfdr26ZX3Xrp0MHTo8cUtE\nv34DGDBgUOIW2OYQZf3Y/OA6DmKxGNFoNPHSdZ0FC+YxffoMXn31LT766CNeeeVNnn32P+zcuYPd\nu7Pp3LkLP/7xpdx//z2sW7cGTdOIRqMsXbqY11+f0+B2Zs68mVdf/Q+ffrqISCRCSUkxf/vbg1RU\nVPCTn8SHf5555tns37+PxYsXoGkalZUVvPjis5x//kRk+dh+mtp5euWVN+vlqSFXXfVLPv74g6QJ\n1WryWvOqudJVe/KtpowePSZpePSWLZvZuHE9mqYRiUR47bVXKCsrZcCA+IQqK1d+TUlJvMd93769\nzJnzH8aOPfQYr+LiIvz+ynozBwvHT916UzPnhGEYSeE1s6w3Vv6DwSCXXfazRLxDhw5j3brVFBYW\n0L17DyA+gmDdujXs2vV9s3vAL730Cp544hmGDBna4PLmltVgMMBXX33B/ff/iSlTLmxwgp0RI0Yx\ncuSZ3H33bezYsR1d1wkGg3z44XvMn/9JA7Em++lPr2Tz5o3s25f8LOEjuY/9rLPGJD19ol+/AXzz\nzZfk5uYAsGrVSg4ePECPHr3o1as3778/j1deeYNXXnmTO+/8M2lp6bzyypu0axe/Erdt2xY6dOjY\n6EGvcGLdffd9zJr1fIPDLydMmMz8+Z+wbdsWAPbv38c777zJxIlTGo2voX17bRdcMJ2ysjK+/PJz\notEoy5d/yp13/jlRZj766CNuvvk2Fi9eUG++meYYM9Bg7a5Dbdpbb32QaJdefvkNAP7xjyc499z4\nyWJ29i6i0SjhcJg33vgvJSUlXHjhRYnvr1u3mjPOGNnskXlC69FY2S8qKmTNmlX84x9PJsrlnDlv\ncuWVVzN/fv2nKzTXNddcz/z5nyQm2I2H/YZNmzby738/R2VlJcFgkHfffYtFixZw441/SKxXs8+2\nW+GFm6M89NBDR52Ow6Wnrvj+/9Ax1fXXz+TNN99P1KlzzjmXiy66OOn2o5r22TRNotFo0u2hsViM\nHTu2M3LkmQhtk81mo1+//rz99htJx0mnnz6Et99+o8n5Deoe69UuPxC/DfWpp57ju+++4emnH0+E\nN3ZcoygK11zzm0bPoWrULev9+w9gw4b1icl8v/9+O5s2radXr0MdAaKsH18/uFa35rmlNfeZTZ06\njbVrVzF79uukpqaRnu7BMOKzwI8efTYLF85l5sybueWW23n33bd4/PG/k5+fh8fjZfDgIY1ORDNh\nwiRsNhtz5rzEI488RCQSSVxFqZmwLDU1lUcfncWzz87iiScexW63M3r0GH73u0PPWz2aq5IFBQWs\nXbuKl19+I2kIad081Y27Z8/eDBt2Bm+88d9EGh5//O88/vjfE+tMmjSV66+/EUmSDt1HqNuQJPj1\nb95Iuge2xpgxY3n66ccpKSkmPT2DWCzKk08+Rl5eDqqq0rNnbx59dFbinsI1a1bxyCMPEggESUtL\nY8qUC5Nmgl+8eAFTp04XB41HpeHytGzZEr74YgVwqG78738f4fP5kCSJq6/+adKymgOkLVs2MXHi\nOUnLPvtsZVL5//vf/4rVamHUqLN47rn/4PUeGv4/aNAQAoEAo0ePSYR5vSn4fKlYrdZ6j7VqjNfr\nTdymA/XrzZYtm5g8eVxSOv/731dp1y4+6c6dd/4RRVGQJJkePXrws59dxYwZP2l0ew899HdefXU2\nf/nL3ZSUlODz+Rgx4kyuuea6Brdfm9Pp4sorr+b55//VZBqfeur5Bidlveiii/nLX+5OPMf7ggum\nk5ubw003/Ra/v4rMzHbcfvufEpMK1d4PeL1eJEkiNTU1EbZ48QIuvrjx/AonwqEy07Fjp+QltcrT\nqFGjueGG3/Pwww9QVFSIz5fGj350MT/60Y8bXB/q79vrLldVlUsv/Slz5rxEJBLGbrczZcqFKIoC\nQHq6h+nTZzB79ot8++3XnHXWOUeUs4vH6Nw928IV1Z9rz7NTk16vNyVxm9GiRfP45JOP0HWdIUOG\n8sQTzyTt75csWXjY+im0Nk2X/UWL5tOnT19GjEh+Nvull17B22+/3uATcBqKv64OHToyZcqFfPTR\nocd7du7chWeffYnnnnuayy67CNOEfv3688QT/2LQoEMXLGrXo35dTDxdulBU1PA8MXfe+UdkWUl8\nHjlyFH/966PNSk9dffr0w+32sG3bFvr3H4jD4cDhcCSW22x2HA5H4paK/Pw8LrvsR0iShCRJTJgw\nhvbtO/LOO/FHtH7xxQqGDz/jqObuEVqPoUPPYMuWzZx+eu2Og2G8//479Z7SUbeNWLp0MUuXLgbi\nxyeZmVm8//48atetrKx2zJr1HL///W+wWm3MmHHJYY+FJk2awmuvvYy/qrzRdaZOncY11/ycaDSK\n1Wpl6NDhXHvtb7j33jspKyvF50vl6quvTXQEiLJ+/EnmsU4/XkdjO83WIjPTc1zysGrVtzzwwJ94\n8snnjvsQmeOVh8bY18dHCjQ0sUmNTz75kL17d3PTTbc2K87G8hCLxbjmmiv517/+Xe/g81STmdn4\nPVSinhx/TZXL1pCHw3nwwXsZP34iP/7x9GPKR1lZGTfd9Ftefvl1LBZLC6aw+RqrK63594HWX8Zq\nHGs+7OsHce8cC+N+8iTnnDOu6S8cxu7du3j00Yd57rnZR/S9tvBbtNV6Aq3796lpazyT9p2wPKxa\ntZIPPniPhx+u3/lwpH7722u466576dGjZ6v+HWqIY69TW908NHWs9uKLz5KamsZll13R4PIjUbus\nH4u28jscDdFxUMfxLAxff/0lRUWFzJhxyXGJv0ZbKdBtIQ+NaQt5E3k4NbSFfLTVE6K28NtA28hH\nW8lDQ1p7vqDt/D4iDyefOPY6tYk8nBqOtuNAjPU+gc4++8iGdwqCIAiCIAiCIAjCyfaDmxxREARB\nEARBEARBEITmEx0HgiAIgiAIgiAIgiA0SnQcCIIgCIIgCMKR0nWk8jKo83g6QRCEtkjMcSAIgiAI\ngiAIzWWaKNm7UPZkg2FiAgzqA+27g6I08WVBEITWSXQcCMesOY9jFIQTTZRLQTg1iLootCmmibph\nHVJ+PqWFBWRY7keq0ikpuA+lzyD0UaNBFgN6BaE5RPvQuog9myAIgiAIgiA0g7JrJ1J+PsX5uXh9\nqUjdVRhoJRWo+OoL5K3iBEgQhLZJdBwIgiAIgiAIQhOkinKU3dmUFRXiS0tHtUcADWQDqVdP0iWZ\nyuXLkA8eONlJFQRBaHHiVgVBEARBEARBOBzTRN2ymZC/CpurAJtvMci5mFKQcNRGSXAOW7kQ++Zy\n7MHP6Xn9NOQ038lOtSAIQosRHQeCIAiCIAiCcBhyXi5SVSW6vAxv5nZMU6KkvB/5VRYsaoQUexE9\ne7zEtvJJFH3nJ6R8xYCZE1GctpOddEEQhBYhblUQBEEQBEEQhMYYBsqunYSjn+BJ34KmpfHtpltY\nufE35Jd0wtQtlO6/Eodq4YwzF+HpDfu/2M7ed1eDYZzs1AuCILQIMeJAOGZiJlThVCTKpSCcGkRd\nFFo7OS8XKboK1bkOw+jE1xv+QGmFC72qgCyuwF8l4XLZyNl9OZ17vcnAc5YQLp/GjrmryTgtnZSz\n+p3sLAjCKUm0D62LGHEgCIIgCIIgCI1Q9q7HkOaiKi7Wbr+O8io3pr+QbmkysiQl1gsHO5G/fxqy\nHGPw5GVYMdn42gr0wpKTmHpBEISWIToOBEEQBEEQBKEBUlERsvYxhhlhd+6PKShrT6SiiC6pSoPr\n+yv6U5w/Dru9koHTviV0sIy9730jblkQBKHVEx0HgiAIgiAIgtAAdc88DGMLlf7e7Ng3Bn9pKV1S\nDv+dssLRVJYNJjUjn57jdpC94nuC3x88MQkWBEE4TkTHgSAIgiAIgiDUIZUXoETeIxiS2bD7F1SV\nV9DFE0FVpKa+ScHBqYQDXejQay9demxg69srxagDQRBaNdFxIAiCIAiCIAh1WHb/h0i0nF37zqW4\nyEEnZwCbpeFbFOoxFXL3XoJppNJ5aDZKcCn5G4uPb4IFQRCOI9FxIBwz+/pB2NcPOtnJEIQkolwK\nwqlB1EWhVarch+lfSmmJh+ycSWRaqnBY6z+MbMjZMxly9swGozB0Bwezr0BSvfQatIb9y+YQCBzv\nhAtC6yHah9ZFdBwIgiAIgiAIQi3q7n8TDOjsyB6H2wjgdRzdE8y1WAq5+6/Eotjpmfk+36/cgGm2\ncGIFQRBOANFxIAiCIAiCIAjVpKrtaCVfU16aRnHJADI8R9dpUCMazqTkwDTscoz00ofYl13RQikV\nhNbL74ecovbsL+hEQYFEOHyyUyQ05dj2hIIgCIIgCILQVpgmxvfPEw0bbNw8ms4+S4tEW64NwbV3\nB65eu8jb9jiVmffjTWlqkkVBaFtME/JzguTsD1JVBdLuvmiGgj8cxWaz4fNJtGtn0KGDid1+slMr\n1CU6DgRBEARBEAQBoHItWskmivPaY6FPM56g0EyyQnnxWbh9RaQ51/L9d4sZNn4KSjPnWhSEVkuv\nhIpvCB5YxcHK7XhDxTjDESTTwNtlJyBxMFBGUVk3ig70pSB7IN+nDaJDJzvduhmkNPH4U+HEER0H\ngiAIgiAIgmAaRLc+jxkz2Lb1DNpnOlo0+rAnC9vWwVjabyRNfpWdW4fRb3BWi25DEE4Z0XzMorno\nOYvRKipQohFCQTcVJW4UPQNVMclotxVkk1R5N2mpu+grf4FuKgQiNop2DGbHwbPxdB5Lr37pWK0n\nO0OC6DgQjll46OaTnQRBqEeUS0E4NYi6KLQWWsEyzIrdHNzdHZ/rtGZ9Z8PXzwLgcjW9rm5zokvt\nCW8LYh+eS8n3/6aw3Z/JyhKzJQptiB5Ey30bI3cuRoUfLWBn/67B5O7vQJq3PRaLE8nmBKC47GoA\nZC2EjQOo8m6szkIcGaX08K4kHFmLdnA2+RVjSO8zGVfWUJDELT4ni+g4EARBEARBEH7YjDCxHbMx\no7B391DaZbXsaIMaYW8W7C6HQTHa2dezaeUqxl04AlUckQttQKzkO2K7X4LyA2gVdnbvGE3B/izS\n0jNo39GHy2UjEIjU+56hOgjRB+gDIRPL95VIge+xpudh61SII/IpkdhKlKL+OHr9EtPR98RnThAd\nB8KRiUYhWKUTLcvB8OeghSsxjBCSGsWQbSA7kC0OJFt7rN6ueH02MbmJIAiCIAintMD376EG8ti1\noy9Z6b2O23YiLh+pqp3S9d3JOqeUrMr/kr1rKH37iUNyofUydJPSbW/hKHoTKRDkwK4hHPi+Fyk+\nH526px9ZZJJEzJkCzpEY4QCx73YTsPtxdD9IVu+1yOFs7F2mYmReBXLLTF4qNI/YSwkNMgwoLZWo\nrIRwRQnWom9wBtfiZAd2tQA7MQzDwDDM6ucRS5iSjK6omIoFw2JFtlqpkDpQbB2ANX0wvs6DsTq8\nJztrgiAIgiAICUa4CPngW4SCNooLhpOefhxPRmSFsK898sGDBP2n47NvZPuGuXTrfrG40CK0SmUl\nEcKbHic1vIxAuZ3tqyejKO1o1ykLSZaPKW7N7kLrPAhnoJTIRifrd/Si35hVpIY/whXajd75VlBT\nWygnQlNEx4GQpKwMDh6UKc4PkBleRIa+kCxpG6ZhYBo6egxKAimEApnEIj7kmB1TUzF1C6qpgaJj\n2BVs3iiqqxzVsw/Ftg8CiwkWWPDb+2Nrdw6ujmeC4jnZ2RVaO9Mg7C8jGgqiBSoxq0rQAhG0iIwR\nk9FMGzFSiKhp4E5DcthRLRJ2u4lhQDAITiccY7smCIIgtGL+TS9gi1Sxc9Mo0tO6Hvfthb2Z+Mpy\nKViTSqdxVrrZP2Lb5vEMGyEurgith2nC3h2leHbfQ4q+jdKidPZvn4DH3RG5eg6DFiFJRN3pyE4f\nHYtz2PLxWLqMXke30Lc4g7dj9rwb0378RgkJh4iOAwGAigrYsUNGy9tG+8gbdLV8iSJFMQ2DYHkW\n/srOaJFeRI3TqF1sdAmwxF86IGtRlIoq/AeLMPUoMbsbW5YCKWXIKQfwpW+EwBaMnJfBOxJnl2ko\nnuZNQCQIsXAlgbx1xMq3oYR3Yg3vQdZCWPUYVl0D00TXa0bCGGBWT6Ajy5hlKrpuJ2p4CdOe/Vv6\nUEF3/NYByO6eeL0SPp9JRoaJzXZy8ykIgiCcGOHCVVhLP6ekIAM9Ohzsx3/iNVNRCad1xJ6XQ1Xp\nKFzer8jLfodA/183a5JFQTjZIhHIXrmVLpX3IumFFOR0ozRvKu7UzOO2TVNWiGV1pWNKiKI1dqoK\nNjLg7C24q26D/n/B9A49btsW4kTHwQ9cJAK7NgTRsr+kC+/jcWxBsploQSdFhUMIVQzBULscdgbT\nIWfPBOIzCxuqFcOTDp50JNPAHaxAO5CHtl8lnDKKoNtNLDUHX4dtpAe/wCz7BsPRD0v7C3F1GAmS\nuPQrJIsEK6g68DVm+SpskY1YYiHUUAg9Av5AKsGqdPSwRFSzo+s2VFXGaYVup32IIusUHByPLAeQ\n5BCyJYLbcgC3sg8iq0k1JYjIRCtdlO85jXz5dHZ7J2LL6EHnztC+vSlGIwjCYZgmaBrEQhpGMIwU\njSBrMayyhkXWAbBtnwIyhId+CXYbpsOJeHi9cCowYxXo22chRTR2bx+JL/XIhzzXHAPt2vCfI/pe\nKKUdvooiir6TcE1209W5hO+3TGPYqPZHnAZBOJHKyiD/s/l0V55AiwXI3zecUHAqNteR3+JT+xyi\nuQybA9+AnlQVdGDdAjeDz/8KX+h2pF53YHSZdMRpEJpPdBz8QBlRjfy1B4nsXEwn21IUew6SESNc\n1Z7S/OFEYyNAUuOjCY6WJBN1pYIrFVWL4qgoIHxgF1JJCsGiCylxB3B1WEu7jE0YVVuJ7OuAlDmV\nlG7nAeI2hh8y04TS3P3E8ubjCH6GI1qFEQwQKM+itHAAoZIM9FgqkqRit3uQHB6QJGQJDB38IVDd\n8wALBcVX1YncwGqUkOIsJBLdi9VdiNNXSEfnd7RjHWZoDqHsdMp3DKPEcy6u3iPp3Mctnh8s/OCZ\nhknl/2/vTqPkOus7j3+fu9Ze3dVdvUqtlmRZluQFY+EVMN5k51iOUQI4wcMSCBwOLxIMYTInw4ST\nSWZeQDLnMCGZwIQhJHBwGGIYwMQY5I2AY8uLttbeaqn3fan9rs+8qG4tdrclWy11dev5nNNH1V1X\nVc+tur/7PPd/t6Ei+f485bEizmQJP1dGq5TQ/SKmPYkVmcSwxrGscXS9iGGWiYY+huXCyx/CMEwM\n3USzo0i7EWk3E8bbkakOZLQVaTSCkVG321IuPikpH/k79OIQR7uuIxG7xEc/Co1idg2pwUPkj28i\n3vkiXt8/Udj8eRKJS9sURTlfPUd85CtfoyP6PZxKwGjvfXjeTWhLUAtONkeZKd3HS8+1csMt3yPt\n/leM6WH8q/+D6kMuElU4uMyI6Snyew9TOvkk6eQugug4UkqKk+so5G6nUlkDCFjkvIWGhdOwGpFp\nJ16Ywh05jJi0kflbGBy8GS3bRUPjPqLlb5Af/WeCVb+B3vQetIiqvF9OXCdk4sTLMPZTkt4r2KUC\nTj7KwMA1lEfbCMIEhqZjJxtBf4urL6Hh6llKxiqKzhbIAzMBkXAE2zxIJN1NMjNIzP5XfOdnaAdj\nTO/fAsmtJDfcRrRzFaqKoFwWKhW8oQmGXvXoPzBMeThP4Ib4fhnH7cEyB0glhonVTxBJTp3xHwUg\nkGi4vom0LQIvhjOkYxoOplHENEYwzGPohoZhGximhm4ZYJlgxQhjHcjoaqTVijRbkVY70l4Dmsqe\nsjjCyadh5JfMjDczOXo1DU2X/hwBL5bGqmuluHuCZEualsgLnOjay9U3XXvJ26Iob8Tz4NBL0zT1\n/ldi9i4qJZ3xgQ/ieUt7bYF0zKegvY1ndzVy+w1fJ+n9DXZ5Gu/6T4Gp7riw2FTh4HIgJWJ0lODw\nSxRHdqJbe4jEypQrOk7+Rgq5W/DcS3RFUqHhJRsQyQailSLBZC/eeEg4vYHp4WsZz5wklX2Jevdf\nMPt/iJa+gXjHbyASW1T1cAXLTxeZOfE00ekfkfL6CEslclPtjPddT2WmHU03sZNNiIt1eLOmU9Ha\nqMg2ZqbvgmmPpHWUWKwLYscxzV+jF/+dsOv/kN+3Cbv+JiKdtxC2roboxbnXt6JcclISTkyRPzpG\nqWeU4kiJSkUiZIFyYT/R9CDpxkGS6UlsS6AbBlIIpDRxylfglJtwnQZcJ4NbaSDw40hpcO0tn6bk\n6HTt/Ry5kqTsh/gYmBSpt0eIm8Mk4tMkYzPE0kVidSViyf0Y9l60iIlmm2BaSMNC2p2E0SuR0Y2E\nsWtBV7tmlbfAHcbt/gYUPA69vJX6hqYla0ox005dcZqpV1eRuXEce+x/kc99hWRKFcmU2pDPQ/ez\n+1nr/DnC7KOYa2Rq5GGCoG6pmwZAIuIhRDs/f/kz3L31b0gG/4j1/BTBDZ9DXTRkcanCwUoWhmiD\nA9D9HOWZpwnlYaQhmSkkKMy8Cy9/PWG4dPf+8SNxaN2I6XtoM0O40wPIsRT54d+mlJ3GTP+axqbn\nkTMvYSY7iK66D5l+l9rjtEJICRODQ1R6f0Kq9DMyzhR+JWRkaAMzg1fiuhmisTSxhqXYMDDJu5vJ\nu5thWmJFRtGNA8Tie4jGXsaffIXC9LfR991ArPVOZOtVhC2tqrqtLCthCIUC5AfyON2DBL1DuDkH\nzy8ggoOI2BDppgEymUl0Q0PXBFJquJU15HOtVEqtVMqtuJUMsHBRTwiIR0pk4i6ZU2O4gFAalN01\nFJx1DI0GOL5E8zwsL48ZTNOQKpBocIhlymSaC8TrxrFTezFiXQjbQuomMnY1QeImwsQ7wEhfio9N\nWe6kj3f8fyKnxjh24BbsSBbNWMJxhaZTal5PZKCLUm8HkdW9DO15lMQ7P6z2lyhLrv9kSPmFb3Gl\n/W18SsyMbSY39VtIWVubkHHbZ01dHT978bPc/Y6vUid/hPl8Hvn2/4TMNCx181aM2vrWlcXhuujH\njyFO/JpK5Wk87wh+qDGVb2dy9B0Y/iaQtXNhKmmYBA0d6DLELkziDR3FykXJGe8mF6kQb9tHa8dh\nvPxJIonvYrVtR2buA03d8Hg58tyAieMvI4Z+TNJ/iVilhFOOMdS7hdzEJoRIEkk0YiZqZcQkcCvN\nQDPlwnvw9VE0ey8t2d1EzWcI+58lOHEFidS70dtvIuzsRCbVLbWU2uN5MD4umJkR5EYdgpMDRCYG\n0ctjII+i68dINA6TSo9jWgamIQCNwFvLzHg75UIH5VI7MlycjSxNVAd7cdun+VRkNFy/noLTxEQ5\nZHi0gOgtIZ0SUb1MPKVT116hqX2SVHMPkfSvMGK7MCIWYeJ6wtQdhPEbQFNFPGUBo/8Xb2Q/00Nr\nGR9aTVPL0h1tMMePxDEa1+DtDolmBokZ/4/xk28n23n1UjdNuUwFARx7aYhM33+j3t6D4wjGB7fj\nlLcuddMWFLUC1jdEeXLXZ3nP2/+GBv8ZjBfGMa75z4Srrljq5q0IQkopF/MFx8byi/lyl1w2m1y+\n81AsoveeJDm9n9HxJwjCw3hSZ2J6PUMDNxOTHRj68rhEfNKUVIb6cEozeHqMvBUhs+ow7RsOEanT\nMFNZzPYdiMzdNTtAzGYXvsDjsl3GZr2VnJTG+ygdfgI7/wuscAzf88hNZxnru4pifgPxaN3i3vf3\nHOJxm2LRecv/vxIE+MZhsk0v0pjqw8Qj9JrQrVtJrno34Zp1hM0tF/0Um2W9zpq1UFZWwnwt9Tx4\nHoyOCoaHBVMjHvbkEGLwEFFnH8l0L/HYMeKpcQxTYFoWoFEptVIqrDlVKIhFkxeUlcXgB4KCY1KY\nKeFNTxI4JSK6RzLr0XblBE3tx4nVj2PELPRkhiBzB2HqdqS9FoSoie/iQq3UnMCly4ooHaC874v4\nQx67nr6H+kwbhrU4/c6F9ikAiZFuilYfmZv3QV0jmRv/B3YisyjtOx8rOSew/LNyqb6fYi5g+Okf\n0Sq+QRBMkZtqZnr0fUh54XvuFyMn5+IFgr5pweYN36ItcwTDzhK54o8Ir7qdxbhV1krPyRtRRxys\nAGJiAv1kD8FQFwXvlxT1wziBxlR+AwO9N2H5naSs5VEwmBNaEbzGDjQgXikSz41SPtzMnoMdNKw/\nxpqN+7HGv4qW/gFm5++gN9wOonaOolAAKfEmjlE5thMx/Txm2EsilFTKcLK/k5nJLehhO1aigWRt\nnCb3pkR0HeRm8oObGR0cI5l5nrZsF7b8EdM9vyA4tpW61jsQ6zYSrloNhlrdKpeG759dLDAnejHH\nnqXD2Ecq3Ud8/SC6HoAAXbeplNdRmO6gVOygXFy1aEcULCZDl9TFXOpiBrQ2ISWUXY3SZJmuX9ez\nx11FQ3aC9k39tHScJJL4Nnrsexipdcjme5F124Hl1Q8qi8yfxD32FeRUjoO7txGJJBetaLBYitlO\nkn1FJrvW03DNEab2fYWmG/8UTd2+VLlEpvYdJDz8Vdoj+6mUPcaHbqVSvJtFv2r6RWTqknUNkoHe\njzE+tZMtnU8SHP4vGIN3Y239FLJOXXj9rVIj2eXK99GGBtF7T+JOniDvPAvmYQJ08oX1DPS8Aztc\nR1rXoPbGgG+KH4lDZC2mlLSXczj9kq6jbSTXHmbNpiPVi6LHAAAeGUlEQVQw/t9x499G6/wQ9qr3\nqIsoLiUpYWwflZ6nYPoFtGAYS0qcSsDgeBvjo+sQ3mYi0SaitTVee8sMHZJkYeY3OT59B2ZiFy3N\nrxKzf0l+7Fe4fdeSyLyT6MYbCDrWQESdYqMsPs+DsTHByDCUh06QLu4iWXiZVr2beHIEfa2HhkQi\n8Jx2ivn1lApzhQJ7qZv/pgkBMTsk1mrT2NpKEEKhuJ4je65g76+maGvtoe3Kk2Ta9mP3H2Dm6Ncg\nci1a+32E2duqtxtWLh+hQ9D9ZfzRXvqP3kBhpo5sU/NSt+p1pKZTaN9IrM+jVDdJXH+R0X2P0vK2\nh5e6acpKJiV+3wkKr36HqPYcvl5kdCRLcWIHQbB8N7IbEh6+fzt7j3Syvv17JLyfEv7yBaym9yI2\nfwCSl+jC8CuI6jmXEykRk5Pog/1oI8NUyv1MFp5Dj50gMDWmc52MDtxEJrKZuPDe6FpVy5MQeLE0\nWixNOgwwCu0cfPIq0qtfpWXDYaIzf0Zh/z/gZB4ivuluIuqKxJeMmO7F7/4p4cQzEIxAIHHLAaOj\nqxkfXYsWbiEWayBqsKLXOhGRhOKdjBy/FaL7yTS9QCK2F895mfIrLVhd7yC65j2IDdeq6yAoF6xU\ngomRAoXRo2gT+0gHe1kbHMAgjzQlWn0IAirFBoqldVScjZSLqwiXYaHgXHQN0smQdLIOqKNYuYKD\nB8qELw6Qbe2iY8MxYnXPog/+Gt1M40VuwWh7F+aad4C58j4P5QwyIDz51zhDXUz3raX36Doas7VX\nNJgTGjaV9s1YXS5BfBJL+zZD1ipaN9++1E1TVpowhP4TlPd/HxE+jSUK5PIm40N3Ebq3spyOMliI\noUvSegd9g59DN59j7aqnCbxvYk//ECt7L+H6h5Dp1qVu5rKxgofwK0iphD40gDYwgCgXyBUOUPF2\noUVGIW4wlWtnbOBmrOBKYkKgL8L5OzVP0/FTDSRSDQT+VZx4qZ94wzM0rTmEWfoLnL5vMiHvQay6\nm/pN7UTr1MBw0eWn4eROvKGdiOAQvh/gOSGDQ51MjKxHk9eSiMdJXIY72DUiUN7K1MnrmbZ7MBN7\naEwfQng/Rvb+FK2/Ay1xPdaVd0LbDYtyzp2y8nmuT270JM7oQZjcR9Q/RCODZFwHGUqkDPDKMfLl\nDkqFZgJ/A563Gilr8zowF1M8EhBvs4C1uP56jp0wcWf205h9hebVR7CjP0ROPY5/MIlvXIOsvwmr\n82YiLe1L3XRlMcmQsPdvqfT+ktJQmqN7rqOuvh7dqO1MBFYUt+U6xKsuxo3PY/IlTpKlY9NmdVCl\ncuGCgPBEL87BH0G4EymnKFZMBvtvRvPeBXLljZmjhgB5OweP3kLEfoa1Hf9OpPTPxCZ/jJV+F17H\n7yKbNy51M2ueKhzUqiBAGxlGG+hHm5wgkANMT79IaJ9AmgHCNhgbv4Lp4RuwwnXYQqyEwuBbY5iY\nmbW4ci3DJwaJJ54lnjlIXP97gp7vMdG1Fc96J1bHJho2NRJpSqnTGd4ikc8R9u5icvI5/MKLBGGZ\nwPOYmMwyNHQVleJW6hJJUivkNIQLJdDBuQLPuYL+iTKhdYhkbD+Zum4st4dw90/RD2aQqbdjr7kZ\n2bgVDHXonFI966c4k6c8cZRgYh9mYR+2f4R4WCLmeQRBiO9ZjE+l8cobqBSzuJVWjMgaEKoQdSbL\nCKlPQzGygZAN9JwI0TlMPLKPusZjRGJPI0rPwbhNkSY8ezMiez326i1E6teoWwAvVzIg6P07nONP\n4owm2PfCbSSSddiRpbjF75sX2DEq2Zsw9laIX/8isWP/ka78l1h3zWZiqo9V3oLQ9ZnZ04t/9Aki\nkafQ9WHKrsbI8FacmXdi6ssjGxciFbEIwm28sHcbdYl/48q1vyJe+Feiuaewjm/Fb/ltgtU3qetS\nLUB9KjVGTE+h9fejjwwig37y+T0EopvQKCCTNhUnztjARkoTm4jqrdiw5AWD6279NAB7fv23S9sQ\nwKeNmcLvUnRmSNW9QCL5ClrsKRBPUe5uZvTw2xDmFuKdm6m7MovW3Ahmbe95WEpByaHcO0I4/DJi\n5mVM7SBCm8ANA/JFm8HBLcxMvZ2YuYqYCbG3dpHWi6KWlksAnSi6ez0V93qOjQXAIVL2qzS2nCSS\n/1fk+E7MqIGMdqBn347WdCNhfAvo8XO+trL8lUuS4vQYzsRBxNQeIpV9ROkjGXiEnkcQQLFQR26i\nnWKxDc9tQ7p1ROP1CN0EAUZ0qedifrWWxYipAZtwg02MjkgQw5jiAFbkKKnMAIbdi17ciRg0qVg2\nod0OqSswG9ZjpTrAbkOaLaqgUMtCj/DIl/B6n8WZrmf387eSTGWIRNOL/lZzy/exPd9Y9NcOzQiF\n1HswukKiV+8iO/RH7Jn6AnVr3snataG6ZI5yTlLC1LDL9Ks9GIM/oz7zHFZsCMeH4dFrKUzegSnS\nmEt0evNS9A+6BqvqoeLdzs5dd5Ot38WWtU+TSv8KO//v2H0bCOrvI+i8D6mug3AWVTioASKfQxse\nRhvuJigco+QeJpSHCTSH0LIIsRgevY7J0Y1oTgcJ2yK60q5fsMh8L83k2Damxu8gkT5KIrWXVF03\nnvskgp8RDiUY61uHZXVg1F1FpH0zVmszsr7+sq0yBm5AYWCG8kAfcvIAevkIFieIxAaQVAgCn2LF\nYHxiLeXSVpxCJ3Hbol7t+XjTYqYObMEPt9B9FLzyUSKRg2SbB6hrOYo90Y3e/RiabRHa65F112G2\n3YhIblYbKyuA40BuJqA83Yec3o+R30Mi6CIux4l7LoEfEPgGE1P15CcbKJVW47stWEYaM5rG0ARG\nBFAbDYtAgGzFk614pbsoFAK0oA+Co2j2MPF0jnimm0jsCMGAQdm00G0DLWIhoi3oqQ60WDvSbENa\nbUirHXR1VNuS8Ty0wd34PX+NX+ojN9HK7hdupTnbjl5jd1A4X6FhMxW5G3FAEL/6RTbIL9LTdR/P\nnfgkLe1p1qwJSS9+PURZ5vJ5GD04ReFANxnvKRrrn4f6YQIpmBq/mnLuPXhOA+ZlvKqKmAGbWgPy\nlbfz9Ms3kkwd5trOp2loOIRVOIw98Q+IyI0ETXcStN0MkRqtzl9Cl+cW0lIrldAmjxOMHMQb6yZ0\nBkEMIOUwvtSRQscJk4yOXktuci1Orp1MIkpKCFh5px1dVFKa5Kc3k5/ejK4XiSVPYMV6sCLdmPoB\nYB8UHsftNqkca0EXLRBZhZlZi5m9AlHfiUzVwQq7FZL0Pcoj/TiDxwmnjkOxB8Pvx7LHsEQJz/OQ\nWogUgsnJOvLTW6g4VyP8NWjCJB23KdpLe1/3lSIeB+IbgA2MjkPP0Rk0DpJqGCDbMkQyuxtrYj9e\nz3fBiOAZawhjG6BuE2b2KsL6q5Z6FpQFhCEU8gGV/ASV3AhBvhctf4SIe4yYdpK4X0J6HlKC69iM\njGUo5lvxKm34lSx2LIOwotgRsFWR4JIQmo7UOsHsJAAKEw5T3Tly5VF0e5RYKk80XSLVUCJZdwLT\nOoTQdYSpo5k6wtDQzATSaoVIO1piNSK+BhldjTSb1d0cLoYwRIyPow/uxxv+BS4v4rkOfb1X0nf8\nnbS1ty11Cy+YNEwmxd34L6VJbPk1mzKPM+P8iiP7tnPi6G/QvGoVnZ0hTU1S1awuY4UCjHfnmDk8\nTHTyBVKRl2hI7EGjTIiOU9jK9PhteK7ai36mZMRjc5tHyV3Hv+/biLTG2bz632hv2k00+iTG9FOY\nJ9OI6DXI+huR2j0gk5dlgVj1YBdD6IAziVYahsIgQX4QWRohKI6AM4ZgkjB0CYIQGQT4UlDxbPLF\nqyjk2ynOtIO7mmRUJykgWUOHfy9nQRAnP70FprcAEtOaRpgjSGOQiNlDPD6Mrvdj+LugouOP6Bim\nCTKF1LMIO4sWbUaLt6KnWpDRLNJuADsNllU7KxDpgzdNUBwnKEzg50YJiyPIygiaM4zmj6HJKXQZ\nEkXiux6BHyARTE2lcEptiLCDMFxLpdxCGFQrrDos+WkxK100CtFoGrgZKWFoIKTn0CSafohUpp/6\nxmHidXvRzb0YYyaiW2PkVRMvrCPUWxCRLFqsET2RxUg2o8Ubq8uoHgctVjvL6HInQwiLEBQhKOCW\n83ilGfziJGFpHFmeQDqTFMQ0whsiHrrEfA/p+0gJYaCRyyUp59pxKu1UCk1ofiN6LAOGiRGhekSB\nsuRC00bPZqknC2xBBB7uWJnjxwrMFByEXiGWzBFP5InX5YjX50ikikTju9G03UD12qdCAzSDkAZC\n0VTtU8wUeqQePZJGj2fArge7Dqwk0opUT6NTF06dn+8gxrsRI3vxhvcQ+icJZR++Lyk6CY4euxfT\n20xzduUMc6VuMFN/E25XO3bd89RtPMlNyX+iEn6XoSOb6T1xA/3N76ShYzXZZrNalFZWLinxShXy\ng+NUhkaojPZiFg4RN3qps7vRGnNIISBMU8i/m9zEtfi+2qB4IzHLZ2Orjxck6B14kN1HtpNtOMaa\nxlfINh7FjjyHNvor/L6/xXMb8fX1yEgHItmJUdeBmW5BxKJg2yt23b381qihA2Fl9hcJhLMPZfXp\nEIJAIkMJSKTrVAd5kuq/yOpEnJ6GMERKCTIkn7NxJqaQoQ+Bh/Q9CH1C34egggjL4JcgLCGDCiIs\nIYICIsyhyRwaBQSV6lvOvpWUEhkGyCDA9SwKxST5QiuVSjOu34L0s9haA6amowOpFX7LutogqhVX\ntx64Cg/IjYWEWh6fCTTRT8wexrIniMdniESG0HWB0DTQdDRdQ9cFmiZA6kgZJ5QpJDGkiCKJwoe/\nOu87F08cpDKRgzCoLiBhtYAkw2B2mfMhdJGBXy0C+F51eQw9CCsIWaouh7JSzUJYQsgSGnk0UUBQ\nPLX8QXWDX5Ph7OHPIaVylHIlgVNOUCkn0WQ7hrUa3228LK++XquEgEhMIxJrBN4JwOR4yHhvERn2\nIo2TRBMTJNMFzOgYdqwXTdfA0EHXCREIMVsr0KrLbnXZjCFFDEQUNLt66oNmI3QbdBuEDbpZ/Vez\nQTORwqruVRUmUjNA6AjNAM2odo6aiRSzvwtR3UqaK1IIDXHG41Mzh0DTBbohILvpdfNfHDpJeaIA\nUiKlRCBnF+q5H06vw2efqy7zc+v0EGR1/S19HyF9ZOghg+pjQq+ar9CD0EP67my+HAhckA4idEC6\nCFlBo4SgiEYFIcrVuxiE1fcUSAxZ/QlDSRiGhKEgCKIUcxGcQgNOJY0M6nErGbxKPZFIBuxqMcdc\nnkdQX5akbiLSJul0ijOPDpe+T2XGZ3rYo1B0cLwipj1DPDlDNDFDPD5DPDlNPDGIafWg6QIhNEJN\nwxfVfAoxm1lNgDSRoQXYSCJIIvDR7y3VbIPrnu5UYP7HCz0fVPA9BxmEwNx4S1YzSnV8VCzFcSby\n1d+lBOkjvBxUJpCVKYQ7ifDHEMEYmpxE+FOEIfh+tW/zQ43xqU4mp67DyV1BXTS6Mo/QFBrlTAeO\n30L5hX7Muv3UrTpOZ2oXfvgqcvx/o+cMvK5WxrVWiDShx7IIK4lmRRFWAmnEQDMQmlkdz2gGUrMQ\nQifipylNlWb7DoHQxKnlUs7uOTCMuW0iAVriwgvSmlb7p4hKCWF+drme3ZaYW8Z9DwJ/9m/VwZcg\nxPdCwqC6vAspkWFY3RYACANE6FfHcIEDQQUZOMjZxyIoVbc1gtLsdkYZGZQZ1T3cSmF2/OcAPnEg\nGoZ4woNEMLu5k6RSvIVC/ipKhTXAytyIvVhMXbKqvsKqevCCTvqHr6Sr2yMe7SebPkCm/gTJVD+G\ncQKRF2hTOuGggYNJGCRBxkBLIrUUwkgitQQYUYQVQ+oRhBUHI4LUbTCN6jhJ1xG6Xh1jIRCieqSz\nEBpSaAhNn83iXN6q/wpNQ859v8JC6NW9Droh3rh2kX1rRaQaT+pruCNYJz+HkO68T0sJE5OCMKj+\nblYK6E5pwZcT8zwuGhr44ezvpzs+fXYFUS0CVDs8OTtQlWFIGGp4bhTHieI4WTw3guPGKTtJvEqK\nwMsQiEZMI0UqpqFrEhMwYbl9CyuWEBq6TKOTBrkOrwxeGabGNcp+iB9MYIhhItY4ljmNZpQx7TK2\nXSISKWDZY2haddkRAg7+/Gk23XPH694n/28fQfizBa8zljFxxkDr9MCqOo08Y2AmOb0cnpoWgetG\ncN0orpPBdaP4TgTXieC4UUI3hS4bMCOtGFa0OjClutxLwFNnHSwLQtPQE0lgC9VrJIDr2kyOl/Cc\nCr43RRCMoelTGHYJK1rCtFxM3cW0HEzTwbTGMCwX3fBPDQyrL366QxKzFQcx+3eYXVLPGCDO92gh\ncqG/C4jEJax9+XXP5Xe+H+1UThZLNT/IM7Mzmycpz8qdPCN/AK5v4Hs2vmvjeXF83ybwbXzXwvct\nvIqN78Xw/RQacXRpkoon8KRdLRBo1UGAHqn+KCuLMAzMpIGZjJAkCTSe9bzvweS4YHhEEIRFZDiJ\nps2gawV0vYBhFjDMIrrhYBguhu5imC6GWcQwPHTd56Xv/YStH9h+yedNP3QQ/eSJt/afxQyB+fc4\npWDhSYD87Nhr7vequSyGhGFY7fOkpFKKUyxnyJfqqFQaKbud4LaRikaIiuqRWytdaFg4mXU4rKPY\n62GHg1iRg+jRUXQ7hxnrxbCOI8oaWm5uR4d2er0OzLfuLhoa+mvWu6dLtVW+gGSyekqEDNYjK++7\nsJkR4N10CzJdd2GvcxEZY99En37i9U+4LmJm+nWdnO+DUxKna2pnPLdQjyk4Y5w31x+F4en+KQQn\ntKg4Jr5vEoRRAt/A90xkGCViNhCEHTiVZjy37g3eSXkzTF3SlHRoSgK0AW34js3xbgfNGMcQwxhi\nENMawYpPY9k5LHsEXQvRRPV7nRtryblxltAQmkA7c6z1Bt/Xmc+8djx19u8avcceplJqxzBgy5Zw\n4TOtOz/wJj+J2bZIKRca0y1bPT095HI5hBDMzMwwNDR86rmzNrhm98oKABmAnN14k2F1RBtWazhC\ngibB0DSEMNA1A00z0XUTw9DRdaO6QlYUqstYKEPCIODOh7a97vl//sdvw9yeSinRASGrKw4hBZoA\nTTNmVygamjDQNH22+lhd6ei6jqZps39XnYPy5kkpCQIf3/dnByYBUvpIGRCGAZLZwfps4UroEMi5\nAgKEZzye+5e5gtRsZyhnH0s4tecKTnd0mqaxfv16NE3jure97az2ffe73ztjA3+u0a+fB6geeSCE\nABmennZ2I+P0/5FoUkMTAoFAAzR0dCEQ6Bi6jj67PjcNQ2VLqUnv+q33LMn7Tk1N0dvbe/bRBa89\n0mB2UDxXgDvzx/N9ek6cQAqBFNrpqvWZGZvL6+yRePghpgRT6JiGjaaZCGFgGCaGoY6OOx9BEOB6\nLgXHwQlcQiGRWnWIy9wPsweJnfqTnN3YofqdauKsryoSjbJmzZqF3/S1y8WZy8qpw+Dm9pRW+4Et\nW7Ys23F0EAR0dXVx1uaUlPi+z/Hu7tkj5iAMqjuWZDh3FDSnjlojlFR7JtClwNQ0DN1C1000zUTT\nNAzTRNdW1vW2LgdhGOL7XnVcJas/Qejh+z5+GOAjCefGURpn76CZGzNJefpotDPXmXO/zx4VdKZo\nLEZHR8cbtu2666570/OzIgsHiqIoiqIoiqIoiqIsjuVZ3lMURVEURVEURVEU5ZJQhQNFURRFURRF\nURRFURakCgeKoiiKoiiKoiiKoixIFQ4URVEURVEURVEURVmQKhwoiqIoiqIoiqIoirKgCyocPPHE\nE2zfvp1NmzbR1dW14HTPPfcc9913H/feey9f//rXL+QtF93MzAwf+9jHuPfee/n4xz9OPp+fd7pN\nmzaxY8cO3vve9/LpT3/6Erdyfuf6XF3X5ZFHHmHbtm089NBDDA4OLkEr39i55uEHP/gBt9xyCzt2\n7GDHjh18//vfX4JWvrE/+ZM/4dZbb+WBBx6Y9/knnniCG2+8kY0bN3Lvvfdy8ODBeadTObk4VE5q\ng8rJaSonF4fKyWm1nBNYvllROakN58oJwCc+8QmuvvpqNm7cyE9+8pMFp6vlrKicLJ3LJSd/8Rd/\nwbZt23jwwQcX7E/OIi9Ad3e37OnpkR/60Ifk/v37550mCAJ59913y/7+fum6rvzN3/xNeezYsQt5\n20X1pS99SX7961+XUkr5ta99TX75y1+ed7rrr7/+UjbrnM7nc/3Od74jv/jFL0oppXz88cflZz7z\nmSVo6cLOZx4ee+wx+ed//udL1MLzs2vXLnngwAG5ffv2eZ9/9NFH5cMPPyw/9KEPyccee0y+//3v\nf900KicXh8pJ7VA5OU3lZPGpnJxW6zmRcnlmReWkdpwrJ88884x8+OGHZU9Pj9yxY4e8//77552u\n1rOicrI0LqecfOITn5BSSrl79+55+5PXuqAjDtatW0dnZydSygWn2bt3L2vWrKG9vR3TNLn//vvZ\nuXPnhbztotq5cyc7duwAYMeOHfziF7+Yd7o3mselcD6f65nzdu+99/L8888vRVMXdL7LRq199q+1\ndetWUqnUgs93dXXxwQ9+ECklV155Jfl8nvHx8bOmUTm5OFROaofKyWm19l2pnNSOyyEnsDyzonJS\nO86Vk507d/LBD36Qzs5O4vE4pVLpdTmB2s+KysnSuJxy8t73vheA6667bt7+5LUu+jUORkZGaG1t\nPfV7c3Mzo6OjF/ttz9vk5CSNjY0AZLNZpqam5p3O8zze97738Tu/8zsLBvdSOp/PdXR0lJaWFgB0\nXSeVSjE9PX1J2/lGznfZePLJJ3nwwQf5wz/8Q4aHhy9lExfFmd8DVOdzZGTkrGlUTi4OlZPlQ+Vk\n6aicLB8rISewPLOicrJ8vDYnmUzmdTmB2s+KysnSuFxzMl9/8lrGuV70937v9+atPjzyyCPceeed\n52xULVRjFpqHz3zmM+f9Gk8//TTZbJa+vj4+8pGPsHHjRlavXr2YzXxTzudzfe00UkqEEBerSW/a\n+czDnXfeyfbt2zFNk0cffZQ//uM/5lvf+tYlaN2b8/nPf54TJ0687jyiRx55ZN75fO33oHJycaic\n1BaVkyqVk8WncnJaLeQEVl5WVE5qT39//7znb1uW9bq/zfc91EJWVE5UTpbK+fQnr3XOwsE3v/nN\nt94ioKWl5ayLXoyMjNDU1HRBr/lmvdE8NDQ0MD4+TmNjI2NjY2QymXmny2azAKxevZqbbrqJgwcP\nLulA73w+15aWFoaHh2lubiYIAgqFAul0+lI3dUHnMw9ntvcDH/gAf/mXf3nJ2vdmfPnLX+ZTn/oU\nP/7xj1/33DPPPHNWJXJ4eHje70rlZPGpnNQWlZMqlZPFp3JyWi3kBFZeVlROas+qVavmzcmf/umf\nnpWTiYmJeTNQC1lROVE5WSrNzc3n7E9ea9FOVVioOnPNNdfQ29vLwMAAruvy+OOPc9dddy3W216w\nO++8k8ceewyoXiFzvrblcjlc1wWqhw298sorrF+//pK287XO53O94447+MEPfgBUr8R88803L0VT\nF3Q+8zA2Nnbq8c6dO7niiisudTPPyxtVJ++66y5++MMfAnD48GFSqdSpQ8/mqJxcHContUXlROXk\nYlE5Oa3WcwLLMysqJ7XlfHNSKBSIx+OvywnUflZUTpbG5ZiT3bt3z9ufzPeCb9nPf/5z+e53v1te\nc8018rbbbpMf//jHpZRSjoyMyE9+8pOnpnv22Wfltm3b5D333CO/9rWvXchbLrqpqSn5kY98RG7b\ntk1+9KMflTMzM1JKKfft2ye/8IUvSCmlfOWVV+T27dvlgw8+KB944AH5L//yL0vZ5FPm+1y/8pWv\nyKeeekpKKaXjOPIP/uAP5D333CPf//73y76+vqVs7rzONQ9/9Vd/Je+//3754IMPyg9/+MPy+PHj\nS9nceX32s5+Vt912m9yyZYu8/fbb5fe//3353e9+Vz766KNSympO3va2t8mNGzfKTZs2yYceekhK\nqXJyqaic1AaVE5WTi03lZHnkRMrlmxWVk9pwrpxIKeXv//7vy02bNsmNGzfKG2+8UW2jXEIqJ7Xh\nfHLyZ3/2Z/Luu++WDzzwwIJ3SDyTkLIGTvBRFEVRFEVRFEVRFKUmXfS7KiiKoiiKoiiKoiiKsnyp\nwoGiKIqiKIqiKIqiKAtShQNFURRFURRFURRFURakCgeKoiiKoiiKoiiKoixIFQ4URVEURVEURVEU\nRVmQKhwoiqIoiqIoiqIoirIgVThQFEVRFEVRFEVRFGVBqnCgKIqiKIqiKIqiKMqC/j+Vtxp7IDt2\nVAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fbc8bd79ad0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "nrows = 17\n",
        "ncols = 5\n",
        "fig, ax = plt.subplots(nrows, ncols, figsize=(18, 21), sharey=True, sharex=True)\n",
        "with warnings.catch_warnings():\n",
        "  warnings.simplefilter('ignore')\n",
        "  ii = -1\n",
        "  for r in range(nrows):\n",
        "    for c in range(ncols):\n",
        "      ii += 1\n",
        "      idx = county_freq[ii, 0]\n",
        "      sns.kdeplot(\n",
        "          posterior_random_weights_final_[:, idx] * log_county_uranium_ppm[idx],\n",
        "          color='blue',\n",
        "          alpha=.3,\n",
        "          shade=True,\n",
        "          label='TFP',\n",
        "          ax=ax[r][c])\n",
        "      sns.kdeplot(\n",
        "          posterior_random_weights_final_stan[:, idx] *\n",
        "          log_county_uranium_ppm[idx],\n",
        "          color='red',\n",
        "          alpha=.3,\n",
        "          shade=True,\n",
        "          label='Stan/rstanarm',\n",
        "          ax=ax[r][c])\n",
        "      sns.kdeplot(\n",
        "          posterior_random_weights_lme4_final_[:, idx] *\n",
        "          log_county_uranium_ppm[idx],\n",
        "          color='#F4B400',\n",
        "          alpha=.7,\n",
        "          shade=False,\n",
        "          label='R/lme4',\n",
        "          ax=ax[r][c])\n",
        "      ax[r][c].vlines(\n",
        "          posterior_random_weights_lme4[idx] * log_county_uranium_ppm[idx],\n",
        "          0,\n",
        "          5,\n",
        "          color='#F4B400',\n",
        "          linestyle='--')\n",
        "      ax[r][c].set_title(county_name[idx] + ' ({})'.format(idx), y=.7)\n",
        "      ax[r][c].set_ylim(0, 5)\n",
        "      ax[r][c].set_xlim(-1., 1.)\n",
        "      ax[r][c].get_yaxis().set_visible(False)\n",
        "      if ii == 2:\n",
        "        ax[r][c].legend(bbox_to_anchor=(1.4, 1.7), fontsize=20, ncol=3)\n",
        "      else:\n",
        "        ax[r][c].legend_.remove()\n",
        "  fig.subplots_adjust(wspace=0.03, hspace=0.1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bv4rVc4Mye7J"
      },
      "source": [
        "## 8  Conclusion"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jNtzJUQXksvZ"
      },
      "source": [
        "In this colab we fit a linear mixed-effect regression model to the radon dataset. We tried three different software packages: R, Stan, and TensorFlow Probability. We concluded by plotting the 85 posterior distributions as computed by the three different software packages. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tsXhZ4rtNUXL"
      },
      "source": [
        "## Appendix A:  Alternative Radon HLM (Add Random Intercept)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F9PqNJQK002P"
      },
      "source": [
        "In this section we describe an alternative HLM which also has a random intercept associated with each county."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qt8a50GYSqbe"
      },
      "source": [
        "$$\\begin{align*}\n",
        "\\text{for } & c=1\\ldots \\text{NumCounties}:\\\\\n",
        "& \\beta_c \\sim \\text{MultivariateNormal}\\left(\\text{loc}=\\left[ \\begin{array}{c} 0 \\\\ 0 \\end{array}\\right] , \\text{scale}=\\left[\\begin{array}{cc} \\sigma_{11}  & 0 \\\\ \\sigma_{12} & \\sigma_{22} \\end{array}\\right] \\right) \\\\\n",
        "\\text{for } & i=1\\ldots \\text{NumSamples}:\\\\\n",
        "& c_i := \\text{County}_i \\\\\n",
        "&\\eta_i = \\underbrace{\\omega_0 + \\omega_1\\text{Floor}_i \\vphantom{\\log( \\text{CountyUraniumPPM}_{c_i}))}}_{\\text{fixed effects}}  + \\underbrace{\\beta_{c_i,0} + \\beta_{c_i,1}\\log( \\text{CountyUraniumPPM}_{c_i}))}_{\\text{random effects}} \\\\\n",
        "&\\log(\\text{Radon}_i) \\sim \\text{Normal}(\\text{loc}=\\eta_i , \\text{scale}=\\sigma)\n",
        "\\end{align*}$$\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oI-DkfJWxK5K"
      },
      "source": [
        " In R's `lme4` \"tilde notation\", this model is equivalent to:\n",
        " > `log_radon ~ 1 + floor + (1 + log_county_uranium_ppm | county)`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H0w7ofFvNsxi"
      },
      "source": [
        "## Appendix B:  Generalized Linear Mixed-Effect Models"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5g4VAJP5xOPZ"
      },
      "source": [
        "In this section we give a more general characterization of Hierarchical Linear Models than what is used in the main body. This more general model is known as a [generalized linear mixed-effect model](https://en.wikipedia.org/wiki/Generalized_linear_mixed_model) (GLMM).\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lA1xwCdENyTx"
      },
      "source": [
        "GLMMs are generalizations of [generalized linear models](https://en.wikipedia.org/wiki/Generalized_linear_model) (GLMs). GLMMs extend GLMs by incorporating sample specific random noise into the predicted linear response.  This is useful in part because it allows rarely seen features to share information with more commonly seen features.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9RJ6ryvUvJ5O"
      },
      "source": [
        "As a generative process, a Generalized Linear Mixed-effects Model (GLMM) is characterized by:\n",
        "\n",
        "\\begin{align}\n",
        "\\text{for } & r = 1\\ldots R:  \\hspace{2.45cm}\\text{# for each random-effect group}\\\\\n",
        " &\\begin{aligned}\n",
        "  \\text{for } &c = 1\\ldots |C_r|:  \\hspace{1.3cm}\\text{# for each category (\"level\") of group $r$}\\\\\n",
        "  &\\begin{aligned}\n",
        "    \\beta_{rc}\n",
        "    &\\sim \\text{MultivariateNormal}(\\text{loc}=0_{D_r}, \\text{scale}=\\Sigma_r^{1/2})\n",
        "  \\end{aligned}\n",
        "\\end{aligned}\\\\\\\\\n",
        "\\text{for } & i = 1 \\ldots N:  \\hspace{2.45cm}\\text{# for each sample}\\\\\n",
        "&\\begin{aligned}\n",
        "  &\\eta_i = \\underbrace{\\vphantom{\\sum_{r=1}^R}x_i^\\top\\omega}_\\text{fixed effects} + \\underbrace{\\sum_{r=1}^R z_{r,i}^\\top \\beta_{r,C_r(i) }}_\\text{random effects} \\\\\n",
        "  &Y_i|x_i,\\omega,\\{z_{r,i} , \\beta_r\\}_{r=1}^R \\sim \\text{Distribution}(\\text{mean}= g^{-1}(\\eta_i))\n",
        "\\end{aligned}\n",
        "\\end{align}\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ycA3XujsN5FJ"
      },
      "source": [
        "where:\n",
        "\n",
        "\\begin{align}\n",
        "R &= \\text{number of random-effect groups}\\\\\n",
        "|C_r| &= \\text{number of categories for group $r$}\\\\\n",
        "N &= \\text{number of training samples}\\\\\n",
        "x_i,\\omega &\\in \\mathbb{R}^{D_0}\\\\\n",
        "D_0 &= \\text{number of fixed-effects}\\\\\n",
        "C_r(i) &= \\text{category (under group $r$) of the $i$th sample}\\\\\n",
        "z_{r,i} &\\in \\mathbb{R}^{D_r}\\\\\n",
        "D_r &= \\text{number of random-effects associated with group $r$}\\\\\n",
        "\\Sigma_{r} &\\in \\{S\\in\\mathbb{R}^{D_r \\times D_r} : S \\succ 0 \\}\\\\\n",
        "\\eta_i\\mapsto g^{-1}(\\eta_i) &= \\mu_i, \\text{inverse link function}\\\\\n",
        "\\text{Distribution} &=\\text{some distribution parameterizable solely by its mean}\n",
        "\\end{align}\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-JAvS_UfN7gl"
      },
      "source": [
        "In words, this says that every category of each group is associated with an iid MVN, $\\beta_{rc}$. Although the $\\beta_{rc}$ draws are always independent, they are only indentically distributed for a group $r$; notice there is exactly one $\\Sigma_r$ for each $r\\in\\{1,\\ldots,R\\}$.\n",
        "\n",
        "When affinely combined with a sample's group's features, $z_{r,i}$, the result is sample-specific noise on the $i$-th predicted linear response (which is otherwise $x_i^\\top\\omega$).\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G4W5ijp1OFPW"
      },
      "source": [
        "When we estimate $\\{\\Sigma_r:r\\in\\{1,\\ldots,R\\}\\}$ we're essentially estimating the amount of noise a random-effect group carries which would otherwise drown out the signal present in $x_i^\\top\\omega$."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Dl2iZkAODfG"
      },
      "source": [
        "There are a variety of options for the $\\text{Distribution}$ and [inverse link function](https://en.wikipedia.org/wiki/Generalized_linear_model#Link_function), $g^{-1}$. Common choices are:\n",
        "- $Y_i\\sim\\text{Normal}(\\text{mean}=\\eta_i, \\text{scale}=\\sigma)$,\n",
        "- $Y_i\\sim\\text{Binomial}(\\text{mean}=n_i \\cdot \\text{sigmoid}(\\eta_i), \\text{total_count}=n_i)$, and, \n",
        "- $Y_i\\sim\\text{Poisson}(\\text{mean}=\\exp(\\eta_i))$.\n",
        "\n",
        "For more possibilities, see the [`tfp.glm`](https://github.com/tensorflow/probability/tree/main/tensorflow_probability/python/glm) module."
      ]
    }
  ],
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